Dynamic graph generation for interactive data analysis

ABSTRACT

Embodiments are directed to visualizing data using a graphical user interface (GUI) that may include a graph panel and a visualization panel arranged to receive inputs or interactions. A data model may be provided and displayed in the visualization panel. Input information that specifies portions of the data model may be provided to the visualization panel. Transform models may be determined based on the specified portions of the data model such that the determined transform models include a model interface that accepts the input information. The transform models may be employed to generate graph objects based on the data model, the input information such that the graph objects may be included in a graph model. Queries based on the graph model may be executed to provide results from the data model such that the results may be displayed in a visualization.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a Utility Patent application based on previouslyfiled U.S. Provisional Patent Application Ser. No. 62/933,305, filed onNov. 8, 2019, the benefit of the filing date of which is hereby claimedunder 35 U.S.C. § 119(e) and which is further incorporated in entiretyby reference.

TECHNICAL FIELD

The present invention relates generally to data visualization, and moreparticularly, but not exclusively to, interactive data analysis.

BACKGROUND

Organizations are generating and collecting an ever increasing amount ofdata. This data may be associated with disparate parts of theorganization, such as, consumer activity, manufacturing activity,customer service, server logs, or the like. For various reasons, it maybe inconvenient for such organizations to effectively utilize their vastcollections of data. In some cases, the quantity of data may make itdifficult to effectively utilize the collected data to improve businesspractices. In some cases, organizations may generate visualizations ofthe some or all of their data. Employing visualizations to representthis data may enable organizations to improve their understanding ofcritical business operations and help them monitor key performanceindicators. However, in some cases, various factors may work againstenabling organizations to generate effective visualizations. Forexample, in some cases, the data might not be in a form or shape that isconducive to the types of analysis that are of interest to theorganization. Also, for example, even if the data is provided in a formor shape that may be useful for one type of analysis, that form or shapemay be disadvantageous for other types of analysis that may be ofinterest to the organization. Thus, is with respect to theseconsiderations and others that the present invention has been made.

BRIEF DESCRIPTION OF THE DRAWINGS

Non-limiting and non-exhaustive embodiments of the present innovationsare described with reference to the following drawings. In the drawings,like reference numerals refer to like parts throughout the variousfigures unless otherwise specified. For a better understanding of thedescribed innovations, reference will be made to the following DetailedDescription of Various Embodiments, which is to be read in associationwith the accompanying drawings, wherein:

FIG. 1 illustrates a system environment in which various embodiments maybe implemented;

FIG. 2 illustrates a schematic embodiment of a client computer;

FIG. 3 illustrates a schematic embodiment of a network computer;

FIG. 4 illustrates a logical architecture of a system for dynamic graphgeneration for interactive data analysis in accordance with one or moreof the various embodiments;

FIG. 5 illustrates a logical representation of a portion of a data modelfor dynamic graph generation for interactive data analysis in accordancewith one or more of the various embodiments;

FIG. 6 illustrates a logical representation of a portion of a data modelfor dynamic graph generation for interactive data analysis in accordancewith one or more of the various embodiments;

FIG. 7A illustrates a logical representation of a portion of a graphmodel for dynamic graph generation for interactive data analysis inaccordance with one or more of the various embodiments;

FIG. 7B illustrates a logical representation of results based on aportion of a graph model for dynamic graph generation for interactivedata analysis in accordance with one or more of the various embodiments;

FIG. 8 illustrates a logical representation of a portion of a userinterface for dynamic graph generation for interactive data analysis inaccordance with one or more of the various embodiments;

FIG. 9 illustrates a logical schematic of a transform model for dynamicgraph generation for interactive data analysis in accordance with one ormore of the various embodiments;

FIG. 10 illustrates a logical schematic of portions of transform modelsfor dynamic graph generation for interactive data analysis in accordancewith one or more of the various embodiments;

FIG. 11 illustrates an overview flowchart for a process for dynamicgraph generation for interactive data analysis in accordance with one ormore of the various embodiments;

FIG. 12 illustrates a flowchart for a process for dynamic graphgeneration for interactive data analysis in accordance with one or moreof the various embodiments;

FIG. 13 illustrates a flowchart for a process for dynamic graphgeneration for interactive data analysis in accordance with one or moreof the various embodiments;

FIG. 14 illustrates a flowchart for a process for dynamic graphgeneration for interactive data analysis in accordance with one or moreof the various embodiments; and

FIG. 15 illustrates a flowchart for a process for dynamic graphgeneration for interactive data analysis in accordance with one or moreof the various embodiments.

DETAILED DESCRIPTION OF VARIOUS EMBODIMENTS

Various embodiments now will be described more fully hereinafter withreference to the accompanying drawings, which form a part hereof, andwhich show, by way of illustration, specific exemplary embodiments bywhich the invention may be practiced. The embodiments may, however, beembodied in many different forms and should not be construed as limitedto the embodiments set forth herein; rather, these embodiments areprovided so that this disclosure will be thorough and complete, and willfully convey the scope of the embodiments to those skilled in the art.Among other things, the various embodiments may be methods, systems,media or devices. Accordingly, the various embodiments may take the formof an entirely hardware embodiment, an entirely software embodiment oran embodiment combining software and hardware aspects. The followingdetailed description is, therefore, not to be taken in a limiting sense.

Throughout the specification and claims, the following terms take themeanings explicitly associated herein, unless the context clearlydictates otherwise. The phrase “in one embodiment” as used herein doesnot necessarily refer to the same embodiment, though it may.Furthermore, the phrase “in another embodiment” as used herein does notnecessarily refer to a different embodiment, although it may. Thus, asdescribed below, various embodiments may be readily combined, withoutdeparting from the scope or spirit of the invention.

In addition, as used herein, the term “or” is an inclusive “or”operator, and is equivalent to the term “and/or,” unless the contextclearly dictates otherwise. The term “based on” is not exclusive andallows for being based on additional factors not described unless thecontext clearly dictates otherwise. In addition, throughout thespecification, the meaning of “a,” “an,” and “the” include pluralreferences. The meaning of “in” includes “in” and “on.”

For example, embodiments, the following terms are also used hereinaccording to the corresponding meaning, unless the context clearlydictates otherwise.

As used herein the term, “engine” refers to logic embodied in hardwareor software instructions, which can be written in a programminglanguage, such as C, C++, Objective-C, COBOL, Java™, PHP, Perl,JavaScript, Ruby, VBScript, Microsoft.NET™ languages such as C#, or thelike. An engine may be compiled into executable programs or written ininterpreted programming languages. Software engines may be callable fromother engines or from themselves. Engines described herein refer to oneor more logical modules that can be merged with other engines orapplications, or can be divided into sub-engines. The engines can bestored in non-transitory computer-readable medium or computer storagedevice and be stored on and executed by one or more general purposecomputers, thus creating a special purpose computer configured toprovide the engine.

As used herein, the term “data source” refers to databases,applications, services, file systems, or the like, that store or provideinformation for an organization. Examples of data sources may include,RDBMS databases, graph databases, spreadsheets, file systems, documentmanagement systems, local or remote data streams, or the like. In somecases, data sources are organized around one or more tables ortable-like structure. In other cases, data sources be organized as agraph or graph-like structure.

As used herein the term “data model” refers to one or more datastructures that provide a representation of an underlying data source.In some cases, data models may provide views of a data source forparticular applications. Data models may be considered views orinterfaces to the underlying data source. In some cases, data models maymap directly to a data source (e.g., practically a logical passthrough). Also, in some cases, data models may be provided by a datasource. In some circumstances, data models may be considered interfacesto data sources. Data models enable organizations to organize or presentinformation from data sources in ways that may be more convenient, moremeaningful (e.g., easier to reason about), safer, or the like.

As used herein the term “data object” refers to one or more entities ordata structures that comprise data models. In some cases, data objectsmay be considered portions of the data model. Data objects may representindividual instances of items or classes or kinds of items.

As used herein the term “graph model” refers to one or more datastructures that may be comprised of one or more nodes and one or moreedges to represent data objects and relationships between or among them.Nodes may be associated with one or more graph objects and edges may beassociated with one or more relationships between the graph objects.

As used herein the term “graph object” refers to one or more entities ordata structures that comprise graph models. In some cases, graph objectsmay be considered portions of a graph model. In some case, graph objectsmay represent individual instances of items or classes or kinds ofitems. In some graph models, the graph objects may be associated withdata objects in a data model.

As used herein the term “panel” refers to region within a graphical userinterface (GUI) that has a defined geometry (e.g., x, y, z-order) withinthe GUI. Panels may be arranged to display information to users or tohost one or more interactive controls. The geometry or styles associatedwith panels may be defined using configuration information, includingdynamic rules. Also, in some cases, users may be enabled to performactions on one or more panels, such as, moving, showing, hiding,re-sizing, re-ordering, or the like.

As user herein the “visualization model” refers to one or more datastructures that represent one or more representations of a data modelthat may be suitable for use in a visualization that is displayed on oneor more hardware displays. Visualization models may define styling oruser interface features that may be made available to non-authoringuser.

As used herein the term “configuration information” refers toinformation that may include rule based policies, pattern matching,scripts (e.g., computer readable instructions), or the like, that may beprovided from various sources, including, configuration files,databases, user input, built-in defaults, or the like, or combinationthereof.

The following briefly describes embodiments of the invention in order toprovide a basic understanding of some aspects of the invention. Thisbrief description is not intended as an extensive overview. It is notintended to identify key or critical elements, or to delineate orotherwise narrow the scope. Its purpose is merely to present someconcepts in a simplified form as a prelude to the more detaileddescription that is presented later.

Briefly stated, various embodiments are directed to visualizing datausing one or more processors that execute one or more instructions toperform as described herein. In one or more of the various embodiments,a graphical user interface (GUI) that may include a graph panel and avisualization panel may be generated such that the graph panel and thevisualization panel are arranged to receive one or more inputs orinteractions.

In one or more of the various embodiments, a data model that includes aplurality of records may be provided such that one or more portions ofthe data model may be displayed in the visualization panel. In one ormore of the various embodiments, providing the data model may include,providing one or more data sources based on one or more of a database, acolumnar data store, or structured text files.

In one or more of the various embodiments, input information may beprovided based on one or more inputs to the visualization panel suchthat the input information specifies one or more portions of the datamodel.

In one or more of the various embodiments, one or more transform modelsmay be determined based on the one or more specified portions of thedata model such that the determined one or more transform models includea model interface that accepts the input information.

In one or more of the various embodiments, the one or more transformmodels may be employed to generate one or more graph objects based onthe data model, the input information, or the like, such that the one ormore graph objects may be included in a graph model that may becomprised of nodes that represent the one or more graph objects and oneor more edges that represent one or more relationships between two ormore graph objects, and such that at least one of the one or morerelationships may be unrepresented in the data model. In one or more ofthe various embodiments, generating the one or more graph objects, mayinclude: determining one or more operations that may be specified by theone or more transform models; and executing the one or more operationson the data model to generate the one or more graph objects.

In one or more of the various embodiments, a query based on the graphmodel may be executed to provide one or more results from the data modelsuch that the one or more results may be displayed in a visualization toone or more users. In one or more of the various embodiments, executingthe query based on the graph model, may include: determining one or moregraph objects that correspond to the query; determining one or morerecords from the data model based on the one or more graph objects thatcorrespond to the query; and generating the one or more results to thequery based on the one or more records.

In one or more of the various embodiments, the one or more transformmodels may be employed to evaluate one or more attributes of a firstportion of the one or more graph objects with one or more otherattributes of a second portion of the one or more graph objects. And, insome embodiments, one or more relationships may be determined based onthe evaluation satisfying one or more conditions specified in the one ormore transform models.

In one or more of the various embodiments, other input information maybe provided based on one or more inputs to the graph panel such that theother input information specifies one or more of the one or more nodesor the one or more edges of the graph model. In one or more of thevarious embodiments, one or more graph transform models may bedetermined based on the other input information. And, in someembodiments, the graph model may be modified based on the one or moregraph transform models.

In one or more of the various embodiments, two or more eligibletransform models may be determined based on the input information. Insome embodiments, the two or more eligible transform models may be rankordered based on a weight score. And, in some embodiments, one or moretransform models may be employed to generate the one or more graphobjects based on the ranked ordering.

Illustrated Operating Environment

FIG. 1 shows components of one embodiment of an environment in whichembodiments of the invention may be practiced. Not all of the componentsmay be required to practice the invention, and variations in thearrangement and type of the components may be made without departingfrom the spirit or scope of the invention. As shown, system 100 of FIG.1 includes local area networks (LANs)/wide area networks(WANs)−(network) 110, wireless network 108, client computers 102-105,visualization server computer 116, or the like.

At least one embodiment of client computers 102-105 is described in moredetail below in conjunction with FIG. 2 . In one embodiment, at leastsome of client computers 102-105 may operate over one or more wired orwireless networks, such as networks 108, or 110. Generally, clientcomputers 102-105 may include virtually any computer capable ofcommunicating over a network to send and receive information, performvarious online activities, offline actions, or the like. In oneembodiment, one or more of client computers 102-105 may be configured tooperate within a business or other entity to perform a variety ofservices for the business or other entity. For example, client computers102-105 may be configured to operate as a web server, firewall, clientapplication, media player, mobile telephone, game console, desktopcomputer, or the like. However, client computers 102-105 are notconstrained to these services and may also be employed, for example, asfor end-user computing in other embodiments. It should be recognizedthat more or less client computers (as shown in FIG. 1 ) may be includedwithin a system such as described herein, and embodiments are thereforenot constrained by the number or type of client computers employed.

Computers that may operate as client computer 102 may include computersthat typically connect using a wired or wireless communications mediumsuch as personal computers, multiprocessor systems, microprocessor-basedor programmable electronic devices, network PCs, or the like. In someembodiments, client computers 102-105 may include virtually any portablecomputer capable of connecting to another computer and receivinginformation such as, laptop computer 103, mobile computer 104, tabletcomputers 105, or the like. However, portable computers are not solimited and may also include other portable computers such as cellulartelephones, display pagers, radio frequency (RF) devices, infrared (IR)devices, Personal Digital Assistants (PDAs), handheld computers,wearable computers, integrated devices combining one or more of thepreceding computers, or the like. As such, client computers 102-105typically range widely in terms of capabilities and features. Moreover,client computers 102-105 may access various computing applications,including a browser, or other web-based application.

A web-enabled client computer may include a browser application that isconfigured to send requests and receive responses over the web. Thebrowser application may be configured to receive and display graphics,text, multimedia, and the like, employing virtually any web-basedlanguage. In one embodiment, the browser application is enabled toemploy JavaScript, HyperText Markup Language (HTML), eXtensible MarkupLanguage (XML), JavaScript Object Notation (JSON), Cascading StyleSheets (CSS), or the like, or combination thereof, to display and send amessage. In one embodiment, a user of the client computer may employ thebrowser application to perform various activities over a network(online). However, another application may also be used to performvarious online activities.

Client computers 102-105 also may include at least one other clientapplication that is configured to receive or send content betweenanother computer. The client application may include a capability tosend or receive content, or the like. The client application may furtherprovide information that identifies itself, including a type,capability, name, and the like. In one embodiment, client computers102-105 may uniquely identify themselves through any of a variety ofmechanisms, including an Internet Protocol (IP) address, a phone number,Mobile Identification Number (MIN), an electronic serial number (ESN), aclient certificate, or other device identifier. Such information may beprovided in one or more network packets, or the like, sent between otherclient computers, visualization server computer 116, or other computers.

Client computers 102-105 may further be configured to include a clientapplication that enables an end-user to log into an end-user accountthat may be managed by another computer, such as visualization servercomputer 116, or the like. Such an end-user account, in one non-limitingexample, may be configured to enable the end-user to manage one or moreonline activities, including in one non-limiting example, projectmanagement, software development, system administration, configurationmanagement, search activities, social networking activities, browsevarious websites, communicate with other users, or the like. Also,client computers may be arranged to enable users to display reports,interactive user-interfaces, or results provided by visualization servercomputer 116.

Wireless network 108 is configured to couple client computers 103-105and its components with network 110. Wireless network 108 may includeany of a variety of wireless sub-networks that may further overlaystand-alone ad-hoc networks, and the like, to provide aninfrastructure-oriented connection for client computers 103-105. Suchsub-networks may include mesh networks, Wireless LAN (WLAN) networks,cellular networks, and the like. In one embodiment, the system mayinclude more than one wireless network.

Wireless network 108 may further include an autonomous system ofterminals, gateways, routers, and the like connected by wireless radiolinks, and the like. These connectors may be configured to move freelyand randomly and organize themselves arbitrarily, such that the topologyof wireless network 108 may change rapidly.

Wireless network 108 may further employ a plurality of accesstechnologies including 2nd (2G), 3rd (3G), 4th (4G) 5th (5G) generationradio access for cellular systems, WLAN, Wireless Router (WR) mesh, andthe like. Access technologies such as 2G, 3G, 4G, 5G, and future accessnetworks may enable wide area coverage for mobile computers, such asclient computers 103-105 with various degrees of mobility. In onenon-limiting example, wireless network 108 may enable a radio connectionthrough a radio network access such as Global System for Mobilcommunication (GSM), General Packet Radio Services (GPRS), Enhanced DataGSM Environment (EDGE), code division multiple access (CDMA), timedivision multiple access (TDMA), Wideband Code Division Multiple Access(WCDMA), High Speed Downlink Packet Access (HSDPA), Long Term Evolution(LTE), and the like. In essence, wireless network 108 may includevirtually any wireless communication mechanism by which information maytravel between client computers 103-105 and another computer, network, acloud-based network, a cloud instance, or the like.

Network 110 is configured to couple network computers with othercomputers, including, visualization server computer 116, clientcomputers 102, and client computers 103-105 through wireless network108, or the like. Network 110 is enabled to employ any form of computerreadable media for communicating information from one electronic deviceto another. Also, network 110 can include the Internet in addition tolocal area networks (LANs), wide area networks (WANs), directconnections, such as through a universal serial bus (USB) port, Ethernetport, other forms of computer-readable media, or any combinationthereof. On an interconnected set of LANs, including those based ondiffering architectures and protocols, a router acts as a link betweenLANs, enabling messages to be sent from one to another. In addition,communication links within LANs typically include twisted wire pair orcoaxial cable, while communication links between networks may utilizeanalog telephone lines, full or fractional dedicated digital linesincluding T1, T2, T3, and T4, or other carrier mechanisms including, forexample, E-carriers, Integrated Services Digital Networks (ISDNs),Digital Subscriber Lines (DSLs), wireless links including satellitelinks, or other communications links known to those skilled in the art.Moreover, communication links may further employ any of a variety ofdigital signaling technologies, including without limit, for example,DS-0, DS-1, DS-2, DS-3, DS-4, OC-3, OC-12, OC-48, or the like.Furthermore, remote computers and other related electronic devices couldbe remotely connected to either LANs or WANs via a modem and temporarytelephone link. In one embodiment, network 110 may be configured totransport information of an Internet Protocol (IP).

Additionally, communication media typically embodies computer readableinstructions, data structures, program modules, or other transportmechanism and includes any information non-transitory delivery media ortransitory delivery media. By way of example, communication mediaincludes wired media such as twisted pair, coaxial cable, fiber optics,wave guides, and other wired media and wireless media such as acoustic,RF, infrared, and other wireless media.

Also, one embodiment of visualization server computer 116 is describedin more detail below in conjunction with FIG. 3 . Although FIG. 1illustrates visualization server computer 116 or the like, as a singlecomputer, the innovations or embodiments are not so limited. Forexample, one or more functions of visualization server computer 116, orthe like, may be distributed across one or more distinct networkcomputers. Moreover, in one or more embodiments, visualization servercomputer 116 may be implemented using a plurality of network computers.Further, in one or more of the various embodiments, visualization servercomputer 116, or the like, may be implemented using one or more cloudinstances in one or more cloud networks. Accordingly, these innovationsand embodiments are not to be construed as being limited to a singleenvironment, and other configurations, and other architectures are alsoenvisaged.

Illustrative Client Computer

FIG. 2 shows one embodiment of client computer 200 that may include manymore or less components than those shown. Client computer 200 mayrepresent, for example, one or more embodiment of mobile computers orclient computers shown in FIG. 1 .

Client computer 200 may include processor 202 in communication withmemory 204 via bus 228. Client computer 200 may also include powersupply 230, network interface 232, audio interface 256, display 250,keypad 252, illuminator 254, video interface 242, input/output interface238, haptic interface 264, global positioning systems (GPS) receiver258, open air gesture interface 260, temperature interface 262,camera(s) 240, projector 246, pointing device interface 266,processor-readable stationary storage device 234, and processor-readableremovable storage device 236. Client computer 200 may optionallycommunicate with a base station (not shown), or directly with anothercomputer. And in one embodiment, although not shown, a gyroscope may beemployed within client computer 200 to measuring or maintaining anorientation of client computer 200.

Power supply 230 may provide power to client computer 200. Arechargeable or non-rechargeable battery may be used to provide power.The power may also be provided by an external power source, such as anAC adapter or a powered docking cradle that supplements or recharges thebattery.

Network interface 232 includes circuitry for coupling client computer200 to one or more networks, and is constructed for use with one or morecommunication protocols and technologies including, but not limited to,protocols and technologies that implement any portion of the OSI modelfor mobile communication (GSM), CDMA, time division multiple access(TDMA), UDP, TCP/IP, SMS, MMS, GPRS, WAP, UWB, WiMax, SIP/RTP, GPRS,EDGE, WCDMA, LTE, UMTS, OFDM, CDMA2000, EV-DO, HSDPA, or any of avariety of other wireless communication protocols. Network interface 232is sometimes known as a transceiver, transceiving device, or networkinterface card (MC).

Audio interface 256 may be arranged to produce and receive audio signalssuch as the sound of a human voice. For example, audio interface 256 maybe coupled to a speaker and microphone (not shown) to enabletelecommunication with others or generate an audio acknowledgment forsome action. A microphone in audio interface 256 can also be used forinput to or control of client computer 200, e.g., using voicerecognition, detecting touch based on sound, and the like.

Display 250 may be a liquid crystal display (LCD), gas plasma,electronic ink, light emitting diode (LED), Organic LED (OLED) or anyother type of light reflective or light transmissive display that can beused with a computer. Display 250 may also include a touch interface 244arranged to receive input from an object such as a stylus or a digitfrom a human hand, and may use resistive, capacitive, surface acousticwave (SAW), infrared, radar, or other technologies to sense touch orgestures.

Projector 246 may be a remote handheld projector or an integratedprojector that is capable of projecting an image on a remote wall or anyother reflective object such as a remote screen.

Video interface 242 may be arranged to capture video images, such as astill photo, a video segment, an infrared video, or the like. Forexample, video interface 242 may be coupled to a digital video camera, aweb-camera, or the like. Video interface 242 may comprise a lens, animage sensor, and other electronics. Image sensors may include acomplementary metal-oxide-semiconductor (CMOS) integrated circuit,charge-coupled device (CCD), or any other integrated circuit for sensinglight.

Keypad 252 may comprise any input device arranged to receive input froma user. For example, keypad 252 may include a push button numeric dial,or a keyboard. Keypad 252 may also include command buttons that areassociated with selecting and sending images.

Illuminator 254 may provide a status indication or provide light.Illuminator 254 may remain active for specific periods of time or inresponse to event messages. For example, when illuminator 254 is active,it may back-light the buttons on keypad 252 and stay on while the clientcomputer is powered. Also, illuminator 254 may back-light these buttonsin various patterns when particular actions are performed, such asdialing another client computer. Illuminator 254 may also cause lightsources positioned within a transparent or translucent case of theclient computer to illuminate in response to actions.

Further, client computer 200 may also comprise hardware security module(HSM) 268 for providing additional tamper resistant safeguards forgenerating, storing or using security/cryptographic information such as,keys, digital certificates, passwords, passphrases, two-factorauthentication information, or the like. In some embodiments, hardwaresecurity module may be employed to support one or more standard publickey infrastructures (PKI), and may be employed to generate, manage, orstore keys pairs, or the like. In some embodiments, HSM 268 may be astand-alone computer, in other cases, HSM 268 may be arranged as ahardware card that may be added to a client computer.

Client computer 200 may also comprise input/output interface 238 forcommunicating with external peripheral devices or other computers suchas other client computers and network computers. The peripheral devicesmay include an audio headset, virtual reality headsets, display screenglasses, remote speaker system, remote speaker and microphone system,and the like. Input/output interface 238 can utilize one or moretechnologies, such as Universal Serial Bus (USB), Infrared, WiFi, WiMax,Bluetooth™, and the like.

Input/output interface 238 may also include one or more sensors fordetermining geolocation information (e.g., GPS), monitoring electricalpower conditions (e.g., voltage sensors, current sensors, frequencysensors, and so on), monitoring weather (e.g., thermostats, barometers,anemometers, humidity detectors, precipitation scales, or the like), orthe like. Sensors may be one or more hardware sensors that collect ormeasure data that is external to client computer 200.

Haptic interface 264 may be arranged to provide tactile feedback to auser of the client computer. For example, the haptic interface 264 maybe employed to vibrate client computer 200 in a particular way whenanother user of a computer is calling. Temperature interface 262 may beused to provide a temperature measurement input or a temperaturechanging output to a user of client computer 200. Open air gestureinterface 260 may sense physical gestures of a user of client computer200, for example, by using single or stereo video cameras, radar, agyroscopic sensor inside a computer held or worn by the user, or thelike. Camera 240 may be used to track physical eye movements of a userof client computer 200.

GPS transceiver 258 can determine the physical coordinates of clientcomputer 200 on the surface of the Earth, which typically outputs alocation as latitude and longitude values. GPS transceiver 258 can alsoemploy other geo-positioning mechanisms, including, but not limited to,triangulation, assisted GPS (AGPS), Enhanced Observed Time Difference(E-OTD), Cell Identifier (CI), Service Area Identifier (SAI), EnhancedTiming Advance (ETA), Base Station Subsystem (BSS), or the like, tofurther determine the physical location of client computer 200 on thesurface of the Earth. It is understood that under different conditions,GPS transceiver 258 can determine a physical location for clientcomputer 200. In one or more embodiments, however, client computer 200may, through other components, provide other information that may beemployed to determine a physical location of the client computer,including for example, a Media Access Control (MAC) address, IP address,and the like.

In at least one of the various embodiments, applications, such as,operating system 206, other client apps 224, web browser 226, or thelike, may be arranged to employ geo-location information to select oneor more localization features, such as, time zones, languages,currencies, calendar formatting, or the like. Localization features maybe used in display objects, data models, data objects, user-interfaces,reports, as well as internal processes or databases. In at least one ofthe various embodiments, geo-location information used for selectinglocalization information may be provided by GPS 258. Also, in someembodiments, geolocation information may include information providedusing one or more geolocation protocols over the networks, such as,wireless network 108 or network 111.

Human interface components can be peripheral devices that are physicallyseparate from client computer 200, allowing for remote input or outputto client computer 200. For example, information routed as describedhere through human interface components such as display 250 or keyboard252 can instead be routed through network interface 232 to appropriatehuman interface components located remotely. Examples of human interfaceperipheral components that may be remote include, but are not limitedto, audio devices, pointing devices, keypads, displays, cameras,projectors, and the like. These peripheral components may communicateover a Pico Network such as Bluetooth™, Zigbee™ and the like. Onenon-limiting example of a client computer with such peripheral humaninterface components is a wearable computer, which might include aremote pico projector along with one or more cameras that remotelycommunicate with a separately located client computer to sense a user'sgestures toward portions of an image projected by the pico projectoronto a reflected surface such as a wall or the user's hand.

A client computer may include web browser application 226 that isconfigured to receive and to send web pages, web-based messages,graphics, text, multimedia, and the like. The client computer's browserapplication may employ virtually any programming language, including awireless application protocol messages (WAP), and the like. In one ormore embodiments, the browser application is enabled to employ HandheldDevice Markup Language (HDML), Wireless Markup Language (WML),WMLScript, JavaScript, Standard Generalized Markup Language (SGML),HyperText Markup Language (HTML), eXtensible Markup Language (XML),HTML5, and the like.

Memory 204 may include RAM, ROM, or other types of memory. Memory 204illustrates an example of computer-readable storage media (devices) forstorage of information such as computer-readable instructions, datastructures, program modules or other data. Memory 204 may store BIOS 208for controlling low-level operation of client computer 200. The memorymay also store operating system 206 for controlling the operation ofclient computer 200. It will be appreciated that this component mayinclude a general-purpose operating system such as a version of UNIX, orLinux®, or a specialized client computer communication operating systemsuch as Windows Phone™, or the Symbian® operating system. The operatingsystem may include, or interface with a Java virtual machine module thatenables control of hardware components or operating system operationsvia Java application programs.

Memory 204 may further include one or more data storage 210, which canbe utilized by client computer 200 to store, among other things,applications 220 or other data. For example, data storage 210 may alsobe employed to store information that describes various capabilities ofclient computer 200. The information may then be provided to anotherdevice or computer based on any of a variety of methods, including beingsent as part of a header during a communication, sent upon request, orthe like. Data storage 210 may also be employed to store socialnetworking information including address books, buddy lists, aliases,user profile information, or the like. Data storage 210 may furtherinclude program code, data, algorithms, and the like, for use by aprocessor, such as processor 202 to execute and perform actions. In oneembodiment, at least some of data storage 210 might also be stored onanother component of client computer 200, including, but not limited to,non-transitory processor-readable removable storage device 236,processor-readable stationary storage device 234, or even external tothe client computer.

Applications 220 may include computer executable instructions which,when executed by client computer 200, transmit, receive, or otherwiseprocess instructions and data. Applications 220 may include, forexample, client visualization engine 222, other client applications 224,web browser 226, or the like. Client computers may be arranged toexchange communications one or more servers.

Other examples of application programs include calendars, searchprograms, email client applications, IM applications, SMS applications,Voice Over Internet Protocol (VOIP) applications, contact managers, taskmanagers, transcoders, database programs, word processing programs,security applications, spreadsheet programs, games, search programs,visualization applications, and so forth.

Additionally, in one or more embodiments (not shown in the figures),client computer 200 may include an embedded logic hardware deviceinstead of a CPU, such as, an Application Specific Integrated Circuit(ASIC), Field Programmable Gate Array (FPGA), Programmable Array Logic(PAL), or the like, or combination thereof. The embedded logic hardwaredevice may directly execute its embedded logic to perform actions. Also,in one or more embodiments (not shown in the figures), client computer200 may include one or more hardware micro-controllers instead of CPUs.In one or more embodiments, the one or more micro-controllers maydirectly execute their own embedded logic to perform actions and accessits own internal memory and its own external Input and Output Interfaces(e.g., hardware pins or wireless transceivers) to perform actions, suchas System On a Chip (SOC), or the like.

Illustrative Network Computer

FIG. 3 shows one embodiment of network computer 300 that may be includedin a system implementing one or more of the various embodiments. Networkcomputer 300 may include many more or less components than those shownin FIG. 3 . However, the components shown are sufficient to disclose anillustrative embodiment for practicing these innovations. Networkcomputer 300 may represent, for example, one embodiment of at least oneof visualization server computer 116, or the like, of FIG. 1 .

Network computers, such as, network computer 300 may include a processor302 that may be in communication with a memory 304 via a bus 328. Insome embodiments, processor 302 may be comprised of one or more hardwareprocessors, or one or more processor cores. In some cases, one or moreof the one or more processors may be specialized processors designed toperform one or more specialized actions, such as, those describedherein. Network computer 300 also includes a power supply 330, networkinterface 332, audio interface 356, display 350, keyboard 352,input/output interface 338, processor-readable stationary storage device334, and processor-readable removable storage device 336. Power supply330 provides power to network computer 300.

Network interface 332 includes circuitry for coupling network computer300 to one or more networks, and is constructed for use with one or morecommunication protocols and technologies including, but not limited to,protocols and technologies that implement any portion of the OpenSystems Interconnection model (OSI model), global system for mobilecommunication (GSM), code division multiple access (CDMA), time divisionmultiple access (TDMA), user datagram protocol (UDP), transmissioncontrol protocol/Internet protocol (TCP/IP), Short Message Service(SMS), Multimedia Messaging Service (MMS), general packet radio service(GPRS), WAP, ultra-wide band (UWB), IEEE 802.16 WorldwideInteroperability for Microwave Access (WiMax), Session InitiationProtocol/Real-time Transport Protocol (SIP/RTP), or any of a variety ofother wired and wireless communication protocols. Network interface 332is sometimes known as a transceiver, transceiving device, or networkinterface card (NIC). Network computer 300 may optionally communicatewith a base station (not shown), or directly with another computer.

Audio interface 356 is arranged to produce and receive audio signalssuch as the sound of a human voice. For example, audio interface 356 maybe coupled to a speaker and microphone (not shown) to enabletelecommunication with others or generate an audio acknowledgment forsome action. A microphone in audio interface 356 can also be used forinput to or control of network computer 300, for example, using voicerecognition.

Display 350 may be a liquid crystal display (LCD), gas plasma,electronic ink, light emitting diode (LED), Organic LED (OLED) or anyother type of light reflective or light transmissive display that can beused with a computer. In some embodiments, display 350 may be a handheldprojector or pico projector capable of projecting an image on a wall orother object.

Network computer 300 may also comprise input/output interface 338 forcommunicating with external devices or computers not shown in FIG. 3 .Input/output interface 338 can utilize one or more wired or wirelesscommunication technologies, such as USB™, Firewire™, WiFi, WiMax,Thunderbolt™, Infrared, Bluetooth™, Zigbee™, serial port, parallel port,and the like.

Also, input/output interface 338 may also include one or more sensorsfor determining geolocation information (e.g., GPS), monitoringelectrical power conditions (e.g., voltage sensors, current sensors,frequency sensors, and so on), monitoring weather (e.g., thermostats,barometers, anemometers, humidity detectors, precipitation scales, orthe like), or the like. Sensors may be one or more hardware sensors thatcollect or measure data that is external to network computer 300. Humaninterface components can be physically separate from network computer300, allowing for remote input or output to network computer 300. Forexample, information routed as described here through human interfacecomponents such as display 350 or keyboard 352 can instead be routedthrough the network interface 332 to appropriate human interfacecomponents located elsewhere on the network. Human interface componentsinclude any component that allows the computer to take input from, orsend output to, a human user of a computer. Accordingly, pointingdevices such as mice, styluses, track balls, or the like, maycommunicate through pointing device interface 358 to receive user input.

GPS transceiver 340 can determine the physical coordinates of networkcomputer 300 on the surface of the Earth, which typically outputs alocation as latitude and longitude values. GPS transceiver 340 can alsoemploy other geo-positioning mechanisms, including, but not limited to,triangulation, assisted GPS (AGPS), Enhanced Observed Time Difference(E-OTD), Cell Identifier (CI), Service Area Identifier (SAI), EnhancedTiming Advance (ETA), Base Station Subsystem (BSS), or the like, tofurther determine the physical location of network computer 300 on thesurface of the Earth. It is understood that under different conditions,GPS transceiver 340 can determine a physical location for networkcomputer 300. In one or more embodiments, however, network computer 300may, through other components, provide other information that may beemployed to determine a physical location of the client computer,including for example, a Media Access Control (MAC) address, IP address,and the like.

In at least one of the various embodiments, applications, such as,operating system 306, modeling engine 322, visualization engine 324,data provider 326, other applications 329, or the like, may be arrangedto employ geo-location information to select one or more localizationfeatures, such as, time zones, languages, currencies, currencyformatting, calendar formatting, or the like. Localization features maybe used in user interfaces, dashboards, visualizations, reports, as wellas internal processes or databases. In at least one of the variousembodiments, geo-location information used for selecting localizationinformation may be provided by GPS 340. Also, in some embodiments,geolocation information may include information provided using one ormore geolocation protocols over the networks, such as, wireless network108 or network 111.

Memory 304 may include Random Access Memory (RAM), Read-Only Memory(ROM), or other types of memory. Memory 304 illustrates an example ofcomputer-readable storage media (devices) for storage of informationsuch as computer-readable instructions, data structures, program modulesor other data. Memory 304 stores a basic input/output system (BIOS) 308for controlling low-level operation of network computer 300. The memoryalso stores an operating system 306 for controlling the operation ofnetwork computer 300. It will be appreciated that this component mayinclude a general-purpose operating system such as a version of UNIX, orLinux®, or a specialized operating system such as MicrosoftCorporation's Windows operating system, or the Apple Corporation'smacOS® operating system. The operating system may include, or interfacewith one or more virtual machine modules, such as, a Java virtualmachine module that enables control of hardware components or operatingsystem operations via Java application programs. Likewise, other runtimeenvironments may be included.

Memory 304 may further include one or more data storage 310, which canbe utilized by network computer 300 to store, among other things,applications 320 or other data. For example, data storage 310 may alsobe employed to store information that describes various capabilities ofnetwork computer 300. The information may then be provided to anotherdevice or computer based on any of a variety of methods, including beingsent as part of a header during a communication, sent upon request, orthe like. Data storage 310 may also be employed to store socialnetworking information including address books, buddy lists, aliases,user profile information, or the like. Data storage 310 may furtherinclude program code, data, algorithms, and the like, for use by aprocessor, such as processor 302 to execute and perform actions such asthose actions described below. In one embodiment, at least some of datastorage 310 might also be stored on another component of networkcomputer 300, including, but not limited to, non-transitory media insideprocessor-readable removable storage device 336, processor-readablestationary storage device 334, or any other computer-readable storagedevice within network computer 300, or even external to network computer300. Data storage 310 may include, for example, data sources 314,visualization models 316, graph models 318, transform models 319, or thelike.

Applications 320 may include computer executable instructions which,when executed by network computer 300, transmit, receive, or otherwiseprocess messages (e.g., SMS, Multimedia Messaging Service (MMS), InstantMessage (IM), email, or other messages), audio, video, and enabletelecommunication with another user of another mobile computer. Otherexamples of application programs include calendars, search programs,email client applications, IM applications, SMS applications, Voice OverInternet Protocol (VOIP) applications, contact managers, task managers,transcoders, database programs, word processing programs, securityapplications, spreadsheet programs, games, search programs, and soforth. Applications 320 may include modeling engine 322, visualizationengine 324, data provider 326, other applications 329, or the like, thatmay be arranged to perform actions for embodiments described below. Inone or more of the various embodiments, one or more of the applicationsmay be implemented as modules or components of another application.Further, in one or more of the various embodiments, applications may beimplemented as operating system extensions, modules, plugins, or thelike.

Furthermore, in one or more of the various embodiments, modeling engine322, visualization engine 324, data provider 326, other applications329, other applications 329, or the like, may be operative in acloud-based computing environment. In one or more of the variousembodiments, these applications, and others, that comprise themanagement platform may be executing within virtual machines or virtualservers that may be managed in a cloud-based based computingenvironment. In one or more of the various embodiments, in this contextthe applications may flow from one physical network computer within thecloud-based environment to another depending on performance and scalingconsiderations automatically managed by the cloud computing environment.Likewise, in one or more of the various embodiments, virtual machines orvirtual servers dedicated to modeling engine 322, visualization engine324, data provider 326, other applications 329, or the like, may beprovisioned and de-commissioned automatically.

Also, in one or more of the various embodiments, modeling engine 322,visualization engine 324, data provider 326, other applications 329, orthe like, may be located in virtual servers running in a cloud-basedcomputing environment rather than being tied to one or more specificphysical network computers.

Further, network computer 300 may also comprise hardware security module(HSM) 360 for providing additional tamper resistant safeguards forgenerating, storing or using security/cryptographic information such as,keys, digital certificates, passwords, passphrases, two-factorauthentication information, or the like. In some embodiments, hardwaresecurity module may be employed to support one or more standard publickey infrastructures (PKI), and may be employed to generate, manage, orstore keys pairs, or the like. In some embodiments, HSM 360 may be astand-alone network computer, in other cases, HSM 360 may be arranged asa hardware card that may be installed in a network computer.

Additionally, in one or more embodiments (not shown in the figures),network computer 300 may include an embedded logic hardware deviceinstead of a CPU, such as, an Application Specific Integrated Circuit(ASIC), Field Programmable Gate Array (FPGA), Programmable Array Logic(PAL), or the like, or combination thereof. The embedded logic hardwaredevice may directly execute its embedded logic to perform actions. Also,in one or more embodiments (not shown in the figures), the networkcomputer may include one or more hardware microcontrollers instead of aCPU. In one or more embodiments, the one or more microcontrollers maydirectly execute their own embedded logic to perform actions and accesstheir own internal memory and their own external Input and OutputInterfaces (e.g., hardware pins or wireless transceivers) to performactions, such as System On a Chip (SOC), or the like.

Illustrative Logical System Architecture

FIG. 4 illustrates a logical architecture of system 400 for dynamicgraph generation for interactive data analysis in accordance with one ormore of the various embodiments. In one or more of the variousembodiments, system 400 may be a visualization platform arranged toinclude various components including: visualization server 402; one ormore data sources, such as, data source 404; one or more data models,such as, as data model 406, one or more visualization models, such as,visualization model 408; one or more graph models, such as, graph model410; one or more transform models, such as, transform models 412; one ormore visualizations, such as, visualization 414; one or more modelingengines, such as, modeling engine 416; one or more visualizationengines, such as, visualization engine 418; or the like.

In one or more of the various embodiments, visualization servers, suchas, visualization server 402 may be arranged to generate one or morevisualizations, such as, visualization 414. In some embodiments,visualization server 402 may be arranged to obtain information from datasources, such as, data source 404. Accordingly, in some embodiments,some or all of the information provided by data source 404 may be mappedto or otherwise extracted and transformed into one or more of one ormore data models or visualization models. Thus, in some embodiments,visualization servers may be arranged to generate one or morevisualizations, such as, visualization 414 based the data models orvisualization models.

In some embodiments, a modeling engine, such as, modeling engine 416 maybe employed to transform some or all of information provided by datasource 404 into one or more data models, such as, data model 406. Insome embodiments, the modeling engine may be arranged to employ orexecute computer readable instructions provided by configurationinformation to determine some or all of the steps for transforminginformation provided by data sources into data models.

In one or more of the various embodiments, configuration information,including user input, may be employed to select one or more portions ofthe information provided by data sources to transform into a data model.

In one or more of the various embodiments, visualization models may becomprised of one or more display objects. In some embodiments, displayobjects may represent a visualization or partial visualization of thedata associated with one or more data objects. The particularvisualization expressed by a display object may be selected based thecomposition (e.g., data type, properties, number of items, semanticmeaning, or the like) of a given data object. In some embodiments, adata object may be associated with more than one display object, eachrepresenting a different visualization of the given data object.Accordingly, display objects may be arranged to represent differentcommon, uncommon, or custom, visualization elements, such as, lineplots, surface plots, bar charts, pie charts, tables, text fields, textareas, or the like, that may be included in visualizations to provideimproved understanding of data. In some embodiments, visualizations maybe targeted for different audiences, such as, customers, stakeholders,internal teams, business intelligence teams, or the like. Accordingly,more than one display model may be generated or associated with the samedata model.

Further, in one or more of the various embodiments, modeling engines,such as, modeling engine 416 may be arranged to enable dynamic graphgeneration for interactive data analysis based on selectivelytransforming information in a data model to provide one or more graphmodels.

Accordingly, in one or more of the various embodiments, modeling enginesmay be arranged to employ one or more transform models, such as,transform models 412 to transform one or more portions of a data model,such as, data model 406 into portions of a graph model, such as, graphmodel 410. Likewise, in some embodiments, other transform models may bearranged to support modifying existing graph models. In one or more ofthe various embodiments, transform models designed for transforminginformation from a data model to a graph model may be referred to asdata transform models. Likewise, in some embodiments, transform modelsdesigned for modifying graph models may be referred to as graphtransform models. Herein, descriptions that refer to transform modelsabsent further qualification may be considered to refer to features orcharacteristics that may be common to both data transform models orgraph transform models.

In some embodiments, modeling engines may be arranged to employ one ormore data transform models to determine transformation operations forone or more provided portions of a data model. In some embodiments, oneor more of the transformations may include generating or modifying oneor more portions of a graph model based on the provided data modelportions. Also, in some embodiments, one or more of the transformationsmay include performing operations that modify, augment, or annotate, thedata model. Further, in some embodiments, transformations or operationsassociated with one or more of the data transform models may be directedto both a data model and a graph model.

Accordingly, in one or more of the various embodiments, modeling enginesmay be arranged to employ one or more data transform models to processor evaluate one more data objects from a data model to generate one ormore graph objects and graph relationships. In some embodiments, graphobjects may be represented as nodes in a graph and relationships betweengraph objects may be represented as edges in the graph.

In some embodiments, modeling engines may be arranged to employ one ormore graph transform models to determine transformation operations forone or more provided portions of a graph model. In some embodiments, oneor more of the transformations may include generating or modifying oneor more portions of the graph model based on one or more of the providedgraph model portions, the associated data model, or the like. Also, insome embodiments, one or more of the transformations may includeperforming operations that modify, augment, or annotate, the data model.Further, in some embodiments, transformations or operations associatedwith one or more graph transform models may be, in some cases, directedto both a data model and a graph model.

In one or more of the various embodiments, visualization engines mayemploy one or more visualization models to generate one or morevisualizations, such as, visualization 414 based on one or more of thegraph model, the data model, or combination thereof. For example, insome embodiments, a visualization platform, such as, system 400, may bearranged to include one or more default visualization models that may beemployed to generate visualizations based on a graph model. Accordingly,in one or more of the various embodiments, visualizations may be updatedin real-time as transform models are employed to modify the graphmodels. Also, in one or more of the various embodiments, one or morecustom or user-authored visualization models may be associated with agraph model such that changes made to graph model may be automaticallyincorporated into visualizations based on the one or more custom oruser-authored visualization models as modifications to a graph modeloccur.

FIG. 5 illustrates a logical representation of a portion of data model500 for dynamic graph generation for interactive data analysis inaccordance with one or more of the various embodiments. As describedabove, one or more data models based on information provided by one ormore data sources may be represented using data models, such as, datamodel 500. In this example, data model 500 is shown as in a tabularformat. Accordingly, for this example, the shape or format of theinformation provided by the data sources may have been tabular. Or, insome cases, one or more ingestion actions (e.g., ETL processes) may havebeen executed on the information provided by the data sources.Similarly, in one or more of the various embodiments, other informationprovided by one or more data sources may have a native shape or formatthat results in non-tabular data model, such as, tree-based models,columnar-based models, structured documents, or the like.

In one or more of the various embodiments, data model 500 may becomprised of one or more tables. In some embodiments, each table mayinclude one or more attributes for one or more types of data objects.For example, in some embodiments, a data model may have one table foreach type of data object. Whereas, in some embodiments, data models mayinclude tables that include attributes for more than one data objecttype.

Also, in one or more of the various embodiments, users of avisualization platform may be enabled to generate new data object typesbased on one or more of fields, objects, values, or the like, includedin the data model.

In this example, table 502 includes several columns that correspond torecord fields of table 502, including, Department Manager (column 504),Executive (column 506), Employee (column 508), Manager (column 510),Employee Title (column 512), Office Location (column 514), or the like.

For example, row 516 includes a record that reveals that C Drigg is adeveloper that is associated with the Seattle office. And, the recordshows that C Drigg's direct manager is F Funge, C Drigg's departmenthead, is S Grand, and that the executive in responsible for C Drigg is GRudd.

FIG. 6 illustrates a logical representation of a portion of data model600 for dynamic graph generation for interactive data analysis inaccordance with one or more of the various embodiments. In this example,data model 600 may be considered an abstract view of some of the dataobjects that may be derived from data model 500. In this example, amodeling engine has provided some data objects based on the columns oftable 502 in data model 500. Accordingly, in this example, for someembodiments, the modeling engine has provided data objects thatcorrespond to the columns in table 502, including, Department Manager(data object 602), Executive (data object 604), Employee (data object606), Manager (data object 608), Employee Job Title (data object 610),Office Location (data object 612), or the like.

In some cases, for one or more of the various embodiments, data model600 may be sufficient for some organizations or some data analysisefforts. However, in many cases, naively providing data objects based ontable columns, or the like, may be disadvantageous for some types ofdata analysis. At least one disadvantage associated with naive dataobject generation may be that the generated objects cannot or do notrepresent one or more concepts that a visualization author is interestedshowing in a visualization. For example, while data model 500 includesinformation regarding relationships between various levels or managementand employees (org chart information), it does not directly provide theconcept of Lead→Report. Accordingly, in this example, data model 600(and data model 500) enables analysis of persons with reports or personswith leads (e.g., managers, department managers, executives) but it doesnot have a native mechanism answering a simple query, such as, showing alist of persons and all the employees that are their responsibility.Further, in this example, a data model, such as, data model 500 or datamodel 600 may be unable to directly represent such as relationship.

Accordingly, conventionally, mismatches between data model shapes anddesired visualizations may be accommodated by enabling users to manuallyprovide custom instructions to modify the shape of a data model to forceit to provide data objects desired for a particular visualization.However, for some embodiments, generating custom instructions to shape adata model (whether by coded instruction, or GUI inputs) may requirehigh levels of skill, including a deep understanding of the data model.

FIG. 7A illustrates a logical representation of a portion of graph model700 for dynamic graph generation for interactive data analysis inaccordance with one or more of the various embodiments. As describedabove, in some embodiments, modeling engines may be arranged to generategraph models from data models. Accordingly, in some embodiments,modeling engines may generate graph models that enable reasoning thatmay be difficult, unsupported or otherwise obscured by the underlyingdata model. Further, in one or more of the various embodiments, modelingengines may be arranged to employ one or more automatic operations thatenable users to interactively generate or modify graph models to furtherexplore or analyze information associated with a data model.

In this example, graph model 700 includes two graph objects, graphobject 702 and graph object 704. In this example, graph object 702represents organization members that are responsible for one or moreother members in the organization. Likewise, in this example, graphobject 704 represents organization members (e.g., employees) that arethe responsibility of a given lead. By observation, it is apparent thatsuch a common or simple question may be difficult to answer directlyfrom data model 500. Namely, in this example, data model 500 hasfeatures that may make it difficult to reason about the Lead→Reportrelationships in the organization. However, as shown here, rearrangingthe information using a graph model, such as, graph model 700 makes ittrivial to reason about the Lead→Report relationship for theorganization.

In conventional systems, users or data administrators may be expected tomanually design graph models, such as, graph model 700. However, inpractice, most users may be unsuited for performing the data analysisthat may be required to design useful graph models. Thus, in many cases,organizations may employ scarce data analysts to design graph models (ordata models) that may be directed to expected analysis use cases. Thispractice may be disadvantageous because of its cost and importantlyinflexibility. Scarce data analyst resources may be expended to generategraph models that later fail to be responsive to unforeseen queries.Accordingly, even if a graph model is well designed for a given set ofassumptions, changes in the underlying assumptions may render a welldesigned graph model obsolete.

FIG. 7B illustrates a logical representation of results based on aportion of graph model 700 for dynamic graph generation for interactivedata analysis in accordance with one or more of the various embodiments.For example, for some embodiments, graph model 700 enables variousresults, all that answer the question “who leads who?” or “who is aperson's lead”.

In this example, result 706 includes the Lead→Report information for AAnery showing that A Anery is (directly or indirectly) responsible foreleven persons. Similarly, in this example: result 708 includes theLead→Report information for G Rudd showing that G Rudd is responsiblefor seven persons; result 710 includes the Lead-Report information for BWills showing that B Wills is responsible for four persons; result 712includes the Lead-Report information for S Grand showing that S Grand isresponsible for three persons; result 714 includes the Lead-Reportinformation for E Funge showing that E Funge is responsible for twopersons; or the like.

While, for this example, the information included in data model 500 issufficient for generating the results (e.g., result 706), the shape ororganization of data model 500 is such that data model 500 obscures theLead→Report relationships that, in contrast, are easily reasoned aboutusing graph model 700.

FIG. 8 illustrates a logical representation of a portion of userinterface 800 for dynamic graph generation for interactive data analysisin accordance with one or more of the various embodiments. In someembodiments, user interface 800 may be arranged to include one or morepanels, such as, panel 802, panel 804, or the like.

In one or more of the various embodiments, user interface 800 may bedisplayed on one or more hardware displays, such as, client computerdisplays, mobile device displays, or the like. In some embodiments, userinterface 800 may be provided via a native application or as a webapplication hosted in a web browser or other similar application. One ofordinary skill in the art will appreciate that for at least clarity orbrevity many details commons to production user interfaces have beenomitted from user interface 800. Likewise, in some embodiments, userinterfaces may be arranged differently than shown depending on localcircumstances. However, one of ordinary skill in the art will appreciatethat the disclosure/description of user interface 800 is at leastsufficient for disclosing the innovations included herein.

In this example, panel 802 is employed to display portions of a graphmodel that may be generated by a modeling engine. In this example, graphmodel 806 may be considered to be the similar to graph model 700described in FIG. 7 . Accordingly, in one or more of the variousembodiments, graph object 808 representing organization members that areleads and graph object 810 may represent the organization members thatreport to a particular lead member. Further, in this example, widget 812represents or references information related to the relationship betweengraph object 808 and graph object 810.

In this example, panel 804 is employed to display portions of data model814. In this example, data model 814 may be considered similar to datamodel 500 in FIG. 5 . However, in some embodiments, user interfaces maybe arranged to enable interactive analysis of included data models. Inthis example, menu 816 represents one of many interactive user interfacecontrols that may be included in a user interface, such as, userinterface 800. In this example, menu 816 may be considered similar to atool-tip, pop-up menu, fly-out menu, or other interactive user interfacecontrol. Accordingly, in some embodiments, user interface controls, suchas, menu 816 may be automatically activated in response to one or moreinteractions or inputs that may be associated with the informationincluded in data model 814. For example, in some embodiments, as usersselect parts (e.g., columns) of data model 814, modeling engines may bearranged to display one or more user interface controls populated withrelevant or context sensitive information, options, or recommendations.

Likewise, in some embodiments, panel 802 may be arranged to enableinteractive inputs. Accordingly, in one or more of the variousembodiments, as users interact with the various graph objects or otheruser interface controls, modeling engines may be arranged provide one ormore user interface controls (e.g., similar to menu 816) that providecontext sensitive information, options, recommendations, or the like.For example, selecting widget 812 may cause a modeling engine togenerate a menu that enables a user to activate or deactivate one ormore filters that may be applied to the relationship between graphobject 808 and graph object 810.

Accordingly, in one or more of the various embodiments, modeling enginesmay be arranged to employ one or more data transform models or graphtransform models to determine one or more of the user interface controlsto enable/display. Likewise, in some embodiments, modeling engines maybe arranged to employ the one or more data transform models or the graphtransform models to determine some or all of the content (e.g., menuitems) to include in the one or more of the user interface controls.

In one or more of the various embodiments, data model features, graphmodel features, selected data objects, selected filters, selected themeinformation, user role information, or the like, may be evaluated orotherwise considered by the one or more transform models. Further, inone or more of the various embodiments, transform models may beclassified or categorized based on various features of the data model.Accordingly, in some embodiments, modeling engines may be arranged toselect eligible transform models based on one or more characteristics ofthe data model or graph model. For example, in some embodiments, datamodel 500 has a tabular database-like shape, thus transform modelsdesigned for evaluating rows, columns, and so on, may be selected toanalyze interaction information associated with data model 814. Incontrast, for example, if the data model of interest is based on XML, orthe like, one or more data transform models suitable or eligible fortabular data models may be automatically excluded from consideration infavor of one or more transform models that are arranged to process XMLdata structures.

FIG. 9 illustrates a logical schematic of transform model 900 fordynamic graph generation for interactive data analysis in accordancewith one or more of the various embodiments. As described above,modeling engines may be arranged to employ one or more data transformmodels or one or more graph transform models that may be arranged toperform various actions based on interactions with data models or graphmodels in an interactive user interface.

In one or more of the various embodiments, described generally,transform model 900 may represent data transform models or graphtransform models. In some embodiments, transform models may beconsidered data structures that include, reference, or encapsulate oneor more components, such as, computer readable instructions, conditions,configuration information, one or more sub-models, or the like.

Further, in one or more of the various embodiments, transform models mayemploy heuristics, machine-learning, pattern-matching, historicalbehavior information, user feedback, or the like, or combinationthereof, to determine if one or more transformations may be available.

In one or more of the various embodiments, data transform models, mayinclude transform models that may be arranged to transform data objectsor other information associated with a data model. In some cases, datatransformations may automatically generate or modify graph objects orgraph models. In other cases, data transformations may modify orannotate the data model rather than modifying a graph model. Also, insome embodiments, a transform model may be considered both a datatransform model or a graph transform model.

Herein, for some embodiments, at least one distinction between datatransform models and graph transform models may be based on the sourceof inputs to the transform model. Accordingly, in some embodiments,transform models that may be arranged to ingest input values associatedwith a data model may be considered data transform models whiletransform models that may be arranged to ingest input values associatedwith a graph model may be considered graph transform models.

In one or more of the various embodiments, the different between datatransform models and graph transform models may be arbitrary. However,herein, they are described separately to help distinguish the types orsource of the input information that may be applied.

In one or more of the various embodiments, transform models, such as,transform model 900 may be arranged to include one or more interfaces,such as, model interface 902. Also, in some embodiments, transformmodels, such as, transform model 900 may be arranged to include at leastone model body, such as, model body 904.

In one or more of the various embodiments, as a modeling engine mayprovide input information associated with one or more interactions, themodeling engine may be arranged to compare the input information to themodel interfaces of one or more transform models. Accordingly, in someembodiments, one or more transform models that have model interfacesthat may be consistent or compatible with the input information may bedetermined.

In one or more of the various embodiments, modeling engines may bearranged to employ one or more of the consistent/compatible transformmodels to evaluate the input information. Accordingly, in one or more ofthe various embodiments, transform models execute one or moretransformations based on the input information provided via a modelinterface, such as, model interface 902. Thus, in some embodiments,heuristics, conditions, instructions, machine-learning models, or thelike, associated with a model body, such as, model body 904 may beemployed to perform or recommend one or more transformations that may beexecuted on the data model or graph model.

FIG. 10 illustrates a logical schematic of portions of transform models1000 for dynamic graph generation for interactive data analysis inaccordance with one or more of the various embodiments. In this example,data transform model 1002 and data transform model 1004 represent one ormore embodiments of transform models that may be employed by modelingengines for determining or execution one or more transformations to datamodels.

Here, in this example, data transform model 1002 includes modelinterface 1006 and model body 1018. Similarly, in this example, for someembodiments, data transform model 1004 includes model interface 1010 andmodel body 1012.

In this example, for some embodiments, model interface 1006 shows thatdata transform model 1002 is expecting inputs provided by a tupleSourcethat may be destined for a specified target. Here in this example, modelinterface 1006 specifies a target that is associated with graph nodes(e.g., graph models).

Further, in this example, for some embodiments, model body 1008specifies one or more operations that may be performed if therequirements of model interface 1006 may be met by the provided inputinformation. In this example, a reference to function called‘addNodesFromTuples’ indicates that data transform model 1002 may bearranged to accept a set of qualified tuples and generated one or morenodes in graph model assuming the various predicates or limitations thatmay be defined in the model body implementation may be satisfied.

In this example, for one or more of the various embodiments, datatransform model 1004 may be considered similar to data transform model1002, in that has a model interface and a model body. However, thedifferences between model interface 1006 and model interface 1010distinguish the two data transform models from each other. Similarly, inthis example, model body 1008 and model body 1012 include differencesthat distinguish data transform model 1002 from data transform model1004. Namely, in this example, model interface 1010 specifies that validinputs to data transform model 1004 should be from a property source(e.g., prop) rather than tuples. Likewise, in this example, model body1012 specifies that the transformation may include generating nodes(e.g., graph objects) based on selected/provided properties of a dataobject rather than from the data object as whole.

Generalized Operations

FIGS. 11-15 represent generalized operations for dynamic graphgeneration for interactive data analysis in accordance with one or moreof the various embodiments. In one or more of the various embodiments,processes 1100, 1200, 1300, 1400, and 1500 described in conjunction withFIGS. 11-15 may be implemented by or executed by one or more processorson a single network computer (or network monitoring computer), such asnetwork computer 300 of FIG. 3 . In other embodiments, these processes,or portions thereof, may be implemented by or executed on a plurality ofnetwork computers, such as network computer 300 of FIG. 3 . In yet otherembodiments, these processes, or portions thereof, may be implemented byor executed on one or more virtualized computers, such as, those in acloud-based environment. However, embodiments are not so limited andvarious combinations of network computers, client computers, or the likemay be utilized. Further, in one or more of the various embodiments, theprocesses described in conjunction with FIGS. 11-15 may be used fordynamic graph generation for interactive data analysis in accordancewith at least one of the various embodiments or architectures such asthose described in conjunction with FIGS. 4-10 . Further, in one or moreof the various embodiments, some or all of the actions performed byprocesses 1100, 1200, 1300, 1400, and 1500 may be executed in part bymodeling engine 322, visualization engine 324, data provider 326 runningon one or more processors of one or more network computers.

FIG. 11 illustrates an overview flowchart for process 1100 for dynamicgraph generation for interactive data analysis in accordance with one ormore of the various embodiments. After a start block, at block 1102, inone or more of the various embodiments, a data model based on a datasource may be provided to a visualization platform

At block 1104, in one or more of the various embodiments, thevisualization platform may be arranged to display the data model. Insome embodiments, the visualization platform may generate a userinterface that includes a visualization panel. In some embodiments, thevisualization panel may be employed to display one or more portions ofthe data model.

At decision block 1106, in one or more of the various embodiments, ifthere may one or more interactions with the data model, control may flowto block 1108; otherwise, control may loop back to decision block 1106.In one or more of the various embodiments, interactions with a userinterface that includes a graph panel or a visualization panel may beprovide input information. In some embodiments, if input information isprovided, the visualization platform may perform one or more additionalactions in response to the input information.

At block 1108, in one or more of the various embodiments, a modelingengine may be arranged to evaluate one or more data model portions thatmay be associated with the input information. In one or more of thevarious embodiments, modeling engines may be arranged to employ one ormore data transform models to evaluate the input information associatedwith the one or more data model portions.

At block 1110, in one or more of the various embodiments, the modelingengine may be arranged to evaluate one or more graph model portions thatmay be associated with the input information. In one or more of thevarious embodiments, modeling engines may be arranged to employ one ormore graph transform models to evaluate the input information associatedwith the one or more graph model portions.

At block 1112, in one or more of the various embodiments, the modelingengine may be arranged to update the graph model based on the inputinformation. In one or more of the various embodiments, modeling enginesmay be arranged to employ one or more graph transform models todetermine modifications to the graph model, if any.

At block 1114, in one or more of the various embodiments, the modelingengine may be arranged to update one or more visualizations based on theupdated graph models. In some embodiments, these updates may be updatesto a visualization of the graph model itself (e.g., panel 802 in FIG. 8), the data model (e.g., panel 804 in FIG. 8 ), or other visualizationsthat may depend on the graph model.

At block 1116, in one or more of the various embodiments, optionally,the modeling engine may be arranged to accept and execute one or morequeries that may be directed to the graph model. Accordingly, in one ormore of the various embodiments, the queries may be executed based onthe graph model. Values associated with the results of the query may beprovided based the data model.

Next, in one or more of the various embodiments, control may be returnedto a calling process.

FIG. 12 illustrates a flowchart for process 1200 for dynamic graphgeneration for interactive data analysis in accordance with one or moreof the various embodiments. After a start block at block 1202, in one ormore of the various embodiments, one or more selected data objects maybe provided to a modeling engine. As described above, one or moreportions of a data model may be displayed in an interactive userinterface. Accordingly, in one or more of the various embodiments, oneor more data objects in the data model may be selected based on one ormore interaction inputs. In some embodiments, rather than being limitedto whole data objects, the selected information may include one or moreproperties or attributes comprising or associated with one or more dataobjects.

In one or more of the various embodiments, data objects may be selectedbased on one or more direct interactions with the data model. Forexample, in some embodiments, a user may be enabled to select one ormore columns from a table based data model. Likewise, in otherembodiments, a user may be enabled to select one or more elements,attributes, fields, or the like from non-tabular data models.

In one or more of the various embodiments, modeling engines may bearranged to aggressively over select the one or more data object basedon given interactions. Likewise, in some embodiments, modeling enginesmay be arranged to include one or more data objects that may be relatedto explicitly selected data object. For example, in some embodiments,the modeling engine may be arranged to recognize one or moreassociations between data objects that may be hidden or obscured from auser.

In one or more of the various embodiments, various interactions, suchas, point selections, area selections, grouping, tagging, marking, orthe like, may be associated with one or more static or dynamic filters,rules, or the like, that may determine which data objects to provide tothe modeling engine. Accordingly, in some embodiments, modeling enginemay be arranged to employ rules, instructions, filters, patternmatching, or the like, provided via configuration information to enablean organization to adapt or modify selection behavior based on localcircumstances. For example, in some embodiments, selection behavior maybe adjusted depending on the input characteristics of the computer ordevice used to display user interface.

At block 1204, in one or more of the various embodiments, the modelingengine may be arranged to determine one or more data transform models.As described above, visualization platforms may include a collection ofdata transform models that may be employed to process interaction inputinformation.

In one or more of the various embodiments, modeling engines may bearranged to provide the one or more data transform models based onvarious criteria. Accordingly, in some embodiments, one or more datatransform models may be restricted to particular data models, users,customers, locations, or the like. For example, in some embodiments,modeling engines may be arranged to employ rules, instructions, filters,or the like, provided via configuration information to make an initialdetermination of data transform models.

In one or more of the various embodiments, modeling engines may bearranged to select all available data transform models as a default. Forexample, in some embodiments, there may be a few data transform modelssuch that testing them all for eligibility (see below) may be of noconcern.

At block 1206, in one or more of the various embodiments, the modelingengine may be arranged to determine one or more eligible data transformmodels based on the data model, the one or more data transform models,or the like. As described above, data transform models may be arrangedto include a model interface that may specify one or morecharacteristics of acceptable/expected inputs.

Accordingly, in some embodiments, modeling engines may be arranged tocompare the model interfaces of the available data transform models todetermine if there may be one or more eligible data transform modelsthat match the input information, such as, the selected data objects, orthe like.

In one or more of the various embodiments, modeling engines or datatransform models may be arranged to such that a common interface may beemployed. Accordingly, in some embodiments, the input information (e.g.,selected data object, or the like) may be provided to each of thedetermined one or more data transform models. Thus, in some embodiments,the respective data transform models may be arranged to interrogate orevaluate the provided input information to determine they may beeligible for performing a data transformation.

Also, in some embodiments, the determination of data transform modeleligibility may be combination of actions performed by the modelingengine or the individual data transform models themselves. Accordingly,in some embodiments, data transform models may be arranged to rejectinput information or otherwise disqualify themselves from being used toexecute transformations.

At block 1208, in one or more of the various embodiments, the modelingengine may be arranged to execute one or more of the datatransformations. In some embodiments, the one or more or more datatransformations may include generating one or more graph objects. Asdescribed above, data transform models include a model body thatspecifies one or more actions that may constitute one or more datatransformations. Accordingly, in some embodiments, the modeling enginemay be arranged to execute one or more actions as specified by one ormore data transform models. Also, in some embodiments, one or more datatransformations may be comprised of one or more sub-transformations.

In some embodiments, different transformations associated with differentdata transform models may be executed. For example, in some embodiments,one or more data transformation models may provide a pipeline of chainedor related transformations based on the input information. In someembodiments, modeling engines may employ configuration information todetermine dependencies or relationships among different transformmodels. Likewise, in some embodiments, data transform models may embeddependency information that may be employed to determine if additionalor related data transform models should be applied.

In some embodiments, one or more data transform models may be associatedwith one or more graph transform models such that executingtransformation for one or more data transform models may trigger orrequire the execution of transformations for one or more graph transformmodels or other data transform models.

In one or more of the various embodiments, data transformations mayinclude scanning a data model for data objects matching specificcriteria. Accordingly, in some embodiments, data model information maybe transformed into graph model information.

At block 1210, in one or more of the various embodiments, the modelingengine may be arranged to provide a graph model based on the one or moredata transformations. In some embodiments, one or more of the datatransformations may produce a new graph model or one or moremodifications to an existing graph model. In some embodiments, one ormore graph objects may be created or modified. Likewise, in someembodiments, one or more relationships between two or more graph objectsmay be created or modified.

At block 1212, in one or more of the various embodiments, thevisualization platform may be arranged to display the graph model in aninteractive user interface. In one or more of the various embodiments,if a new graph model may have been created or an existing graph modelhas been modified, a visualization engine in the visualization platformmay be arranged to update the user interface to show the new or updatedgraph model.

Next, in one or more of the various embodiments, control may be returnedto a calling process.

FIG. 13 illustrates a flowchart for process 1300 for dynamic graphgeneration for interactive data analysis in accordance with one or moreof the various embodiments. In some embodiments, process 1300 may beconsidered similar to process 1200 described above. However, process1300 is focused on activity associated with interactive inputs directedtowards a graph model rather than a data model. After a start block atblock 1302, in one or more of the various embodiments, one or moreselected graph objects may be provided to a modeling engine. Asdescribed above, one or more graph objects, graph object properties,graph object relationships, or the like, may be determined to beselected based on one or more interactive inputs.

Accordingly, in one or more of the various embodiments, one or moregraph objects in the graph model may be selected based on one or moreinteraction inputs. In some embodiments, rather than being limited towhole graph objects, the selected information may include one or moreproperties or attributes comprising or associated with one or more graphobjects.

In one or more of the various embodiments, graph objects may be selectedbased on one or more direct interactions with the graph model. Forexample, in some embodiments, a user may be enabled to select one ormore columns from a table based graph model. Likewise, in otherembodiments, a user may be enabled to select one or more elements,attributes, fields, or the like from non-tabular graph models.

In one or more of the various embodiments, modeling engines may bearranged to aggressively over select the one or more graph object basedon given interactions. Likewise, in some embodiments, modeling enginesmay be arranged to include one or more graph objects that may be relatedto explicitly selected graph object. For example, in some embodiments,the modeling engine may be arranged to recognize one or moreassociations between graph objects that may be hidden or obscured from auser.

In one or more of the various embodiments, various interactions, suchas, point selections, area selections, grouping, tagging, marking, orthe like, may be associated with one or more static or dynamic filters,rules, or the like, that may determine which graph objects to provide tothe modeling engine. Accordingly, in some embodiments, modeling enginemay be arranged to employ rules, instructions, filters, patternmatching, or the like, provided via configuration information to enablean organization to adapt or modify selection behavior based on localcircumstances. For example, in some embodiments, selection behavior maybe adjusted depending on the input characteristics of the computer ordevice used to display user interface.

At block 1304, in one or more of the various embodiments, the modelingengine may be arranged to determine one or more graph transform models.As described above, visualization platforms may include a collection ofgraph transform models that may be employed to process interaction inputinformation.

In one or more of the various embodiments, modeling engines may bearranged to provide the one or more graph transform models based onvarious criteria. Accordingly, in some embodiments, one or more graphtransform models may be restricted to particular graph models, datamodels, users, customers, locations, or the like. For example, in someembodiments, modeling engines may be arranged to employ rules,instructions, filters, or the like, provided via configurationinformation to make an initial determination of graph transform models.

In one or more of the various embodiments, modeling engines may bearranged to select all available graph transform models as a default.For example, in some embodiments, there may be a few graph transformmodels such that testing them all for eligibility (see below) may be ofno concern.

At block 1306, in one or more of the various embodiments, the modelingengine may be arranged to determine one or more eligible graph transformmodels based on the graph model, the one or more graph transform models,the underlying data model, or the like. As described above, graphtransform models may be arranged to include a model interface that mayspecify one or more characteristics of acceptable/expected inputs.

Accordingly, in some embodiments, modeling engines may be arranged tocompare the model interfaces of the available graph transform models todetermine if there may be one or more eligible graph transform modelsthat match the input information, such as, the selected graph objects,or the like.

In one or more of the various embodiments, modeling engines or graphtransform models may be arranged to such that a common interface may beemployed. Accordingly, in some embodiments, the input information (e.g.,selected graph object, or the like) may be provided to each of thedetermined one or more graph transform models. Thus, in someembodiments, the respective graph transform models may be arranged tointerrogate or evaluate the provided input information to determine theymay be eligible for performing a graph transformation.

Also, in some embodiments, the determination of graph transform modeleligibility may be combination of actions performed by the modelingengine or the individual graph transform models themselves. Accordingly,in some embodiments, graph transform models may be arranged to rejectinput information or otherwise disqualify themselves from being used toexecute transformations.

At block 1308, in one or more of the various embodiments, the modelingengine may be arranged to execute one or more of the graphtransformations. In some embodiments, the one or more or more graphtransformations may include generating one or more graph objects. Asdescribed above, graph transform models include a model body thespecifies one or more actions that may constitute one or more graphtransformations. Accordingly, in some embodiments, the modeling enginemay be arranged to execute one or more actions as specified by one ormore graph transform models. Also, in some embodiments, one or moregraph transformations may be comprised of one or moresub-transformations.

In some embodiments, different transformations associated with differentgraph transform models may be executed. For example, in someembodiments, one or more graph transformation models may provide apipeline of chained or related transformations based on the inputinformation.

In some embodiments, modeling engines may employ configurationinformation to determine dependencies or relationships among differenttransform models. Likewise, in some embodiments, graph transform modelsmay embed dependency information that may be employed to determine ifadditional or related graph transform models should be applied.

In some embodiments, one or more graph transform models may beassociated with one or more graph transform models such that executingtransformations for one or more graph transform models may trigger orrequire the execution of transformations for one or more graph transformmodels or other graph transform models.

In one or more of the various embodiments, graph transformations mayinclude scanning a graph model for graph objects matching specificcriteria. Accordingly, in some embodiments, graph model information maybe transformed into graph model information.

At block 1310, in one or more of the various embodiments, the modelingengine may be arranged to provide a graph model based on the one or moregraph transformations. In some embodiments, one or more of the graphtransformations may produce one or more modifications to an existinggraph model. In some embodiments, one or more graph objects may becreated or modified. Likewise, in some embodiments, one or morerelationships between two or more graph objects may be created ormodified.

At block 1312, in one or more of the various embodiments, thevisualization platform may be arranged to display the graph model in aninteractive user interface. In one or more of the various embodiments,if an existing graph model has been modified, a visualization engine inthe visualization platform may be arranged to update the user interfaceto show the updated graph model.

Next, in one or more of the various embodiments, control may be returnedto a calling process.

FIG. 14 illustrates a flowchart for process 1400 for dynamic graphgeneration for interactive data analysis in accordance with one or moreof the various embodiments. After a start block, at block 1402, in oneor more of the various embodiments, one or more selected data objectsmay be provided to a modeling engine. As described above, interactiveinput information that includes or references one or more data objectsor data object properties may be determined based on the interactiveinput information.

At block 1404, in one or more of the various embodiments, the modelingengine may be arranged to compare one or more data transform modelinterfaces to the selected data objects or the interactive inputinformation. As described above, data transform models include modelinterfaces that may be arranged to specify how or what information maybe passed to a data transform model.

In one or more of the various embodiments, model interfaces may specifyinput type information, input source information, user/rolerequirements, or the like. In some embodiments, model interfaces may beconsidered similar to a function signature that a compiler or linker mayevaluate to determine if a function defined in a library is callable.For example, if the function signature for an API requires two integersand one floating number, attempts to call that function with two stringparameters is unlikely to recognized as a valid call.

Accordingly, in one or more of the various embodiments, model interfacesmay at once define the necessary input parameters for a data transformmodel as well as enabling modeling engines to determine if a giventransform model may be eligible to process provided interactive inputs.

Further, in one or more of the various embodiments, model interfaces maybe arranged to specify additional constraints or requirements todetermine if corresponding data transform models may be eligible in agiven circumstance. For example, in some embodiments, model interfacesmay include value based constraints as well as type based constraints.Accordingly, for example, a model interface may specify a require typeor a range of acceptable values.

In one or more of the various embodiments, model interfaces may bearranged to execute various computer readable instructions, conditionchecks, pattern matching, or the like, provided via configurationinformation. Accordingly, in some embodiments, data model transforms ortheir model interfaces may be modified or adapted to localcircumstances.

At decision block 1406, in one or more of the various embodiments, ifone or more eligible data transform models may be determined, controlmay flow to decision block 1408; otherwise, control may be returned to acalling process.

In some embodiments, all data transform models may be considered ordetermined ineligible even though one or more data objects may have beenselected based on interactive inputs. Accordingly, in one or more of thevarious embodiments, visualization platforms may be arranged to providea user a cue (e.g., visual or audio) that the current selection will notresult in a data transformation. Generally, this indicates that thecurrent selection cannot be mapped to a data transform model. In one ormore of the various embodiments, a remedy may include modifying theselection, modifying one or more data transform models, adding oractivating one or more data transform models.

At decision block 1408, in one or more of the various embodiments, ifthere is more than one eligible data transform model, control may flowto block 1510; otherwise, control may flow to block 1514. In someembodiments, two or more data transform models may be determined to beeligible. For example, if the model interfaces for two or more differentdata transform models may be satisfied by the current input informationthe two or more different data transform models may be selected here.

At block 1410, in one or more of the various embodiments, the modelingengine may be arranged to rank order two or more data transform models.In one or more of the various embodiments, as described above, datatransform models may be associated with scores that may be employed toweight the two or more data transform models against each other.

In one or more of the various embodiments, data transform models may beassociated with one or more other scores, such as, priority scores,quality of match scores, confidence scores, quantitized scores (e.g.,high, med, low, etc.), compound scores generated based on a formula, orthe like. Accordingly, in one or more of the various embodiments,modeling engines may be arranged to rank the two or more data transformmodels based on the one or more ranking scores.

At block 1412, in one or more of the various embodiments, optionally,the modeling engine may be arranged to display the rank ordered list ofdata transform models. In some embodiments, visualization platforms maybe arranged to display a popup menu, or the like, that enables a user tosee some or all of the rank ordered list. Accordingly, in someembodiments, the user may be enabled to manually select a lower-rankedtransformation over a higher ranked transformation. Also, in someembodiments, modeling engines may be arranged to employ the top-rankedtransformation if a user does not make a selection or confirmation.

Likewise, in one or more of the various embodiments, modeling enginesmay be arranged to require affirmative selections/confirmations by usersbefore particular transformations may be executed. Thus, in someembodiments, even if a transformation is the top ranked transformation,in some cases, the modeling engine may require express confirmationbefore executing the transformation.

In some embodiments, requiring express confirmation may depend on one ormore values, such as, confidence scores, impact scores, or the like. Insome embodiments, modeling engines may be arranged to employ rules,instructions, or the like, provided via configuration information toadapt or modify express confirmation requirements based on localcircumstances.

Note, for some embodiments, this step may be optional because in somecases, the modeling engine may be arranged to automatically select adata transform model from among the rank ordered list of data transformmodels without displaying a list to a user.

At block 1414, in one or more of the various embodiments, the modelingengine may be arranged to execute one or more graph transformation onthe graph model based on one or more graph transform models. Asdescribed above, each data transform model may include a model body thatincludes computer readable instructions, computer readable data, or thelike. In some embodiments, model bodies may include references tocomputer readable instructions, libraries, plugins, or services that maybe employed to execute a data transformation.

In one or more of the various embodiments, two or more datatransformations may be executed. In some embodiments, two or more datatransformations may be independent enabling the two or more datatransformations to be executed simultaneously or concurrently.

In one or more of the various embodiments, modeling engines may bearranged to perform one or more actions based on the specificationsincluded by or referenced by the model bodies that correspond to thedetermined data transform models.

In some embodiments, if two or more data transform models have beendetermined to be eligible, the corresponding transformations may beattempted, one after the other, until a first transformation hasexecuted successfully. Accordingly, in some embodiments, modelingengines may try each data transform model in order until the first onehas performed its transformation. Thus, in one or more of the variousembodiments, each eligible data transform models may attempt a datatransformation until the first one succeeds.

Accordingly, in one or more of the various embodiments, modeling enginesmay be arranged to rely on the data transform model to be the finalarbiter with respect to if a transformation may occur. In someembodiments, one or more data transform models may be arranged to applyother/additional input beyond the interactive input information (e.g.,the selected data objects) that may be provided. In some embodiments,data transform models may be arranged to employ one or more externalservices to provide or evaluate information that may be relevant to thetransformation attempted by the data transform model.

For example, in some embodiments, a data transform model may be arrangedto evaluate if a data object may be an employee of an organizationrather than a customer. For example, a transformation step may includegenerating a particular node or relationship in the graph model if thedata object represents an employee rather than a customer. In thisexample, the data transform model may be arranged to submit information(e.g., name, address, or the like) included in the provided data objectsto an organization's directory service that can confirm if the emailaddress corresponds to an employee. Thus, in this example, if the dataobject information represents an employee, the transformation mayexecute. Or, if the data object information represents a customer thetransformation fails enabling a subsequent data transform model toattempt a data transformation.

Note, in one or more of the various embodiments, data transform modelsmay be described as executing or performing actions associated with datatransformation. However, in some embodiments, the modeling engine may bearranged to execute one or more actions on behalf of data transformmodels based on information (e.g., instructions) included in the datatransform models.

Next, in one or more of the various embodiments, control may be returnedto a calling process.

FIG. 14 illustrates a flowchart for process 1400 for dynamic graphgeneration for interactive data analysis in accordance with one or moreof the various embodiments. After a start block, at block 1402, in oneor more of the various embodiments, one or more selected data objectsmay be provided to a modeling engine. As described above, interactiveinput information that includes or references one or more data objectsor data object properties may be determined based on the interactiveinput information.

At block 1404, in one or more of the various embodiments, the modelingengine may be arranged to compare one or more data transform modelinterfaces to the selected data objects or the interactive inputinformation. As described above, data transform models include modelinterfaces that may be arranged to specify how or what information maybe passed to a data transform model.

In one or more of the various embodiments, model interfaces may specifyinput type information, input source information, user/rolerequirements, or the like. In some embodiments, model interfaces may beconsidered similar to a function signature that a compiler or linker mayevaluate to determine if a function defined in a library is callable.For example, if the function signature for an API requires two integersand one floating number, attempts to call that function with two stringparameters is unlikely to recognized as a valid call.

Accordingly, in one or more of the various embodiments, model interfacesmay at once define the necessary input parameters for a data transformmodel as well as enabling modeling engines to determine if a giventransform model may be eligible to process provided interactive inputs.

Further, in one or more of the various embodiments, model interfaces maybe arranged to specify additional constraints or requirements todetermine if corresponding data transform models may be eligible in agiven circumstance. For example, in some embodiments, model interfacesmay include value based constraints as well as type based constraints.Accordingly, for example, a model interface may specify a require typeor a range of acceptable values.

In one or more of the various embodiments, model interfaces may bearranged to execute various computer readable instructions, conditionchecks, pattern matching, or the like, provided via configurationinformation. Accordingly, in some embodiments, data model transforms ortheir model interfaces may be modified or adapted to localcircumstances.

At decision block 1406, in one or more of the various embodiments, ifone or more eligible data transform models may be determined, controlmay flow to decision block 1408; otherwise, control may be returned to acalling process.

In some embodiments, all data transform models may be considered ordetermined ineligible even though one or more data objects may have beenselected based on interactive inputs. Accordingly, in one or more of thevarious embodiments, visualization platforms may be arranged to providea user a cue (e.g., visual or audio) that the current selection will notresult in a data transformation. Generally, this indicates that thecurrent selection cannot be mapped to a data transform model. In one ormore of the various embodiments, a remedy may include modifying theselection, modifying one or more data transform models, adding oractivating one or more data transform models.

At decision block 1408, in one or more of the various embodiments, ifthere is more than one eligible data transform model, control may flowto block 1510; otherwise, control may flow to block 1514. In someembodiments, two or more data transform models may be determined to beeligible. For example, if the model interfaces for two or more differentdata transform models may be satisfied by the current input informationthe two or more different data transform models may be selected here.

At block 1410, in one or more of the various embodiments, the modelingengine may be arranged to rank order two or more data transform models.In one or more of the various embodiments, as described above, datatransform models may be associated with scores that may be employed toweight the two or more data transform models against each other.

In one or more of the various embodiments, data transform models may beassociated with one or more other scores, such as, priority scores,quality of match scores, confidence scores, quantitized scores (e.g.,high, med, low, etc.), compound scores generated based on a formula, orthe like. Accordingly, in one or more of the various embodiments,modeling engines may be arranged to rank the two or more data transformmodels based on the one or more ranking scores.

At block 1412, in one or more of the various embodiments, optionally,the modeling engine may be arranged to display the rank ordered list ofdata transform models. In some embodiments, visualization platforms maybe arranged to display a popup menu, or the like, that enables a user tosee some or all of the rank ordered list. Accordingly, in someembodiments, the user may be enabled to manually select a lower-rankedtransformation over a higher ranked transformation. Also, in someembodiments, modeling engines may be arranged to employ the top-rankedtransformation if a user does not make a selection or confirmation.

Likewise, in one or more of the various embodiments, modeling enginesmay be arranged to require affirmative selections/confirmations by usersbefore particular transformations may be executed. Thus, in someembodiments, even if a transformation is the top ranked transformation,in some cases, the modeling engine may require express confirmationbefore executing the transformation.

In some embodiments, requiring express confirmation may depend on one ormore values, such as, confidence scores, impact scores, or the like. Insome embodiments, modeling engines may be arranged to employ rules,instructions, or the like, provided via configuration information toadapt or modify express confirmation requirements based on localcircumstances.

Note, for some embodiments, this step may be optional because in somecases, the modeling engine may be arranged to automatically select adata transform model from among the rank ordered list of data transformmodels without displaying a list to a user.

At block 1414, in one or more of the various embodiments, the modelingengine may be arranged to execute one or more graph transformation onthe graph model based on one or more graph transform models. Asdescribed above, each data transform model may include a model body thatincludes computer readable instructions, computer readable data, or thelike. In some embodiments, model bodies may include references tocomputer readable instructions, libraries, plugins, or services that maybe employed to execute a data transformation.

In one or more of the various embodiments, two or more datatransformations may be executed. In some embodiments, two or more datatransformations may be independent enabling the two or more datatransformations to be executed simultaneously or concurrently.

In one or more of the various embodiments, modeling engines may bearranged to perform one or more actions based on the specificationsincluded by or referenced by the model bodies that correspond to thedetermined data transform models.

In some embodiments, if two or more data transform models have beendetermined to be eligible, the corresponding transformations may beattempted, one after the other, until a first transformation have beenexecuted successfully. Accordingly, in some embodiments, modelingengines may try each data transform model in order until the first onehas performed its transformation. Thus, in one or more of the variousembodiments, each eligible data transform models may attempt a datatransformation until the first one succeeds.

Accordingly, in one or more of the various embodiments, modeling enginesmay be arranged to rely on the data transform model to be the finalarbiter with respect to if a transformation may occur. In someembodiments, one or more data transform models may be arranged to applyother/additional input beyond the interactive input information (e.g.,the selected data objects) that may be provided. In some embodiments,data transform models may be arranged to employ one or more externalservices to provide or evaluate information that may be relevant to thetransformation attempted by the data transform model.

For example, in some embodiments, a data transform model may be arrangedto evaluate if a data object may be an employee of an organizationrather than a customer. For example, a transformation step may includegenerating a particular node or relationship in the graph model if thedata object represents an employee rather than a customer. In thisexample, the data transform model may be arranged to submit information(e.g., name, address, or the like) included in the provided data objectsto an organization's directory service that can confirm if the emailaddress corresponds to an employee. Thus, in this example, if the dataobject information represents an employee, the transformation mayexecute. Or, if the data object information represents a customer thetransformation fails enabling a subsequent data transform model toattempt a data transformation.

Note, in one or more of the various embodiments, data transform modelsmay be described as executing or performing actions associated with datatransformation. However, in some embodiments, the modeling engine may bearranged to execute one or more actions on behalf of data transformmodels based on information (e.g., instructions) included in the datatransform models.

Next, in one or more of the various embodiments, control may be returnedto a calling process.

FIG. 15 illustrates a flowchart for process 1500 for dynamic graphgeneration for interactive data analysis in accordance with one or moreof the various embodiments. After a start block at block 1502, in one ormore of the various embodiments, one or more selected graph objects maybe provided to a modeling engine.

At block 1504, in one or more of the various embodiments, the modelingengine may be arranged to compare one or more graph transform modelinterfaces to the selected graph objects.

At decision block 1506, in one or more of the various embodiments, ifone or more eligible graph transform models may be determined, controlmay flow to decision block 1508; otherwise, control may be returned to acalling process.

At decision block 1508, in one or more of the various embodiments, ifthere is more than one eligible graph transform model, control may flowto block 1510; otherwise, control may flow to block 1514.

At block 1510, in one or more of the various embodiments, the modelingengine may be arranged to rank order two or more graph transform models.

At block 1512, in one or more of the various embodiments, optionally,the modeling engine may be arranged to display the rank ordered list ofgraph transform models.

Note, for some embodiments, this step may be optional because in somecases, the modeling engine may be arranged to automatically select agraph transform model from among the rank ordered list of graphtransform models without displaying a list to a user.

At block 1514, in one or more of the various embodiments, the modelingengine may be arranged to execute one or more graph transformation onthe graph model based on one or more graph transform models.

Next, in one or more of the various embodiments, control may be returnedto a calling process.

It will be understood that each block in each flowchart illustration,and combinations of blocks in each flowchart illustration, can beimplemented by computer program instructions. These program instructionsmay be provided to a processor to produce a machine, such that theinstructions, which execute on the processor, create means forimplementing the actions specified in each flowchart block or blocks.The computer program instructions may be executed by a processor tocause a series of operational steps to be performed by the processor toproduce a computer-implemented process such that the instructions, whichexecute on the processor, provide steps for implementing the actionsspecified in each flowchart block or blocks. The computer programinstructions may also cause at least some of the operational steps shownin the blocks of each flowchart to be performed in parallel. Moreover,some of the steps may also be performed across more than one processor,such as might arise in a multi-processor computer system. In addition,one or more blocks or combinations of blocks in each flowchartillustration may also be performed concurrently with other blocks orcombinations of blocks, or even in a different sequence than illustratedwithout departing from the scope or spirit of the invention.

Accordingly, each block in each flowchart illustration supportscombinations of means for performing the specified actions, combinationsof steps for performing the specified actions and program instructionmeans for performing the specified actions. It will also be understoodthat each block in each flowchart illustration, and combinations ofblocks in each flowchart illustration, can be implemented by specialpurpose hardware-based systems, which perform the specified actions orsteps, or combinations of special purpose hardware and computerinstructions. The foregoing example should not be construed as limitingor exhaustive, but rather, an illustrative use case to show animplementation of at least one of the various embodiments of theinvention.

Further, in one or more embodiments (not shown in the figures), thelogic in the illustrative flowcharts may be executed using an embeddedlogic hardware device instead of a CPU, such as, an Application SpecificIntegrated Circuit (ASIC), Field Programmable Gate Array (FPGA),Programmable Array Logic (PAL), or the like, or combination thereof. Theembedded logic hardware device may directly execute its embedded logicto perform actions. In one or more embodiments, a microcontroller may bearranged to directly execute its own embedded logic to perform actionsand access its own internal memory and its own external Input and OutputInterfaces (e.g., hardware pins or wireless transceivers) to performactions, such as System On a Chip (SOC), or the like.

What is claimed as new and desired to be protected by Letters Patent of the United States is:
 1. A method for visualizing data using a computer that includes one or more processors, where each step of the method is performed by the one or more processors, comprising: generating a user interface that includes a graph panel and a visualization panel, wherein the graph panel and the visualization panel are arranged to receive one or more inputs or interactions; providing a data model that includes a plurality of records, wherein one or more portions of the data model are displayed in the visualization panel; providing input information based on one or more inputs to the visualization panel, wherein the input information specifies one or more portions of the data model; determining one or more transform models based on the one or more specified portions of the data model, wherein the determined one or more transform models include a model interface that accepts the input information based on one or more acceptable characteristics of inputs from the one or more specified portions of the data model; employing the model interface of the one or more transform models to determine eligibility of each transform model to transform one or more data objects for the one or more portions of the data model; in response the model interface determining the one or more transform models are ineligible to provide transformations for the one or more data objects, employing the user interface to provide one or more of playing an audio cue or displaying a visual cue and provide one or more recommendations to remedy the ineligibility that are determined by the model interface; employing one or more eligible transform models to perform further actions, including: generating one or more graph objects based on the data model and the input information, wherein the one or more graph objects are included in a graph model that is comprised of nodes that represent the one or more graph objects and one or more edges that represent one or more relationships between two or more graph objects, and wherein at least one of the one or more relationships is unrepresented in the data model; and evaluating one or more attributes of a first portion of the one or more graph objects with one or more other attributes of a second portion of the one or more graph objects; and determining the one or more relationships based on the evaluation satisfying one or more conditions specified in the one or more eligible transform models; and executing a query based on the graph model to provide one or more results from the data model, wherein the one or more results are displayed in a visualization to one or more users.
 2. The method of claim 1, wherein generating the one or more graph objects, further comprises: determining one or more operations that are specified by the one or more eligible transform models; and executing the one or more operations on the data model to generate the one or more graph objects.
 3. The method of claim 1, wherein providing the data model, further comprises, providing one or more data sources based on one or more of a database, a columnar data store, or structured text files.
 4. The method of claim 1, further comprising: providing other input information based on one or more inputs to the graph panel, wherein the other input information specifies one or more of the one or more nodes or the one or more edges of the graph model; determining one or more graph transform models based on the other input information; and modifying the graph model based on the one or more graph transform models.
 5. The method of claim 1, wherein executing the query based on the graph model, further comprises: determining one or more graph objects that correspond to the query; determining one or more records from the data model based on the one or more graph objects that correspond to the query; and generating the one or more results to the query based on the one or more records.
 6. The method of claim 1, further comprising: determining two or more eligible transform models based on the input information; rank ordering the two or more eligible transform models based on a weight score; and employing one or more transform models to generate the one or more graph objects based on the ranked ordering.
 7. A processor readable non-transitory storage media that includes instructions for visualizing data, wherein execution of the instructions by one or more processors, performs actions, comprising: generating a graphical user interface (GUI) that includes a graph panel and a visualization panel, wherein the graph panel and the visualization panel are arranged to receive one or more inputs or interactions; providing a data model that includes a plurality of records, wherein one or more portions of the data model are displayed in the visualization panel; providing input information based on one or more inputs to the visualization panel, wherein the input information specifies one or more portions of the data model; determining one or more transform models based on the one or more specified portions of the data model, wherein the determined one or more transform models include a model interface that accepts the input information based on one or more acceptable characteristics of inputs from the one or more specified portions of the data model; employing the model interface of the one or more transform models to determine eligibility of each transform model to transform one or more data objects for the one or more portions of the data model; in response the model interface determining the one or more transform models are ineligible to provide transformations for the one or more data objects, employing the user interface to provide one or more of playing an audio cue or displaying a visual cue and provide one or more recommendations to remedy the ineligibility that are determined by the model interface; employing one or more eligible transform models to perform further actions, including: generating one or more graph objects based on the data model and the input information, wherein the one or more graph objects are included in a graph model that is comprised of nodes that represent the one or more graph objects and one or more edges that represent one or more relationships between two or more graph objects, and wherein at least one of the one or more relationships is unrepresented in the data model; and evaluating one or more attributes of a first portion of the one or more graph objects with one or more other attributes of a second portion of the one or more graph objects; and determining the one or more relationships based on the evaluation satisfying one or more conditions specified in the one or more eligible transform models; and executing a query based on the graph model to provide one or more results from the data model, wherein the one or more results are displayed in a visualization to one or more users.
 8. The media of claim 7, wherein generating the one or more graph objects, further comprises: determining one or more operations that are specified by the one or more eligible transform models; and executing the one or more operations on the data model to generate the one or more graph objects.
 9. The media of claim 7, wherein providing the data model, further comprises, providing one or more data sources based on one or more of a database, a columnar data store, or structured text files.
 10. The media of claim 7, further comprising: providing other input information based on one or more inputs to the graph panel, wherein the other input information specifies one or more of the one or more nodes or the one or more edges of the graph model; determining one or more graph transform models based on the other input information; and modifying the graph model based on the one or more graph transform models.
 11. The media of claim 7, wherein executing the query based on the graph model, further comprises: determining one or more graph objects that correspond to the query; determining one or more records from the data model based on the one or more graph objects that correspond to the query; and generating the one or more results to the query based on the one or more records.
 12. The media of claim 7, further comprising: determining two or more eligible transform models based on the input information; rank ordering the two or more eligible transform models based on a weight score; and employing one or more transform models to generate the one or more graph objects based on the ranked ordering.
 13. A system for visualizing data: a network computer, comprising: a transceiver that communicates over the network; a memory that stores at least instructions; and one or more processors that execute instructions that perform actions, including: generating a graphical user interface (GUI) that includes a graph panel and a visualization panel, wherein the graph panel and the visualization panel are arranged to receive one or more inputs or interactions; providing a data model that includes a plurality of records, wherein one or more portions of the data model are displayed in the visualization panel; providing input information based on one or more inputs to the visualization panel, wherein the input information specifies one or more portions of the data model; determining one or more transform models based on the one or more specified portions of the data model, wherein the determined one or more transform models include a model interface that accepts the input information based on one or more acceptable characteristics of inputs from the one or more specified portions of the data model; employing the model interface of the one or more transform models to determine eligibility of each transform model to transform one or more data objects for the one or more portions of the data model; in response the model interface determining the one or more transform models are ineligible to provide transformations for the one or more data objects, employing the user interface to provide one or more of playing an audio cue or displaying a visual cue and provide one or more recommendations to remedy the ineligibility that are determined by the model interface; employing one or more eligible transform models to perform further actions, including: generating one or more graph objects based on the data model and the input information, wherein the one or more graph objects are included in a graph model that is comprised of nodes that represent the one or more graph objects and one or more edges that represent one or more relationships between two or more graph objects, and wherein at least one of the one or more relationships is unrepresented in the data model; and evaluating one or more attributes of a first portion of the one or more graph objects with one or more other attributes of a second portion of the one or more graph objects; and determining the one or more relationships based on the evaluation satisfying one or more conditions specified in the one or more eligible transform models; and executing a query based on the graph model to provide one or more results from the data model, wherein the one or more results are displayed in a visualization to one or more users; and a client computer, comprising: a transceiver that communicates over the network; a memory that stores at least instructions; and one or more processors that execute instructions that perform actions, including: providing one or more portions of the input information.
 14. The system of claim 13, wherein generating the one or more graph objects, further comprises: determining one or more operations that are specified by the one or more eligible transform models; and executing the one or more operations on the data model to generate the one or more graph objects.
 15. The system of claim 13, wherein providing the data model, further comprises, providing one or more data sources based on one or more of a database, a columnar data store, or structured text files.
 16. The system of claim 13, wherein the one or more processors of the network computer execute instructions that perform actions, further comprising: providing other input information based on one or more inputs to the graph panel, wherein the other input information specifies one or more of the one or more nodes or the one or more edges of the graph model; determining one or more graph transform models based on the other input information; and modifying the graph model based on the one or more graph transform models.
 17. The system of claim 13, wherein executing the query based on the graph model, further comprises: determining one or more graph objects that correspond to the query; determining one or more records from the data model based on the one or more graph objects that correspond to the query; and generating the one or more results to the query based on the one or more records.
 18. The system of claim 13, wherein the one or more processors of the network computer execute instructions that perform actions, further comprising: determining two or more eligible transform models based on the input information; rank ordering the two or more eligible transform models based on a weight score; and employing one or more transform models to generate the one or more graph objects based on the ranked ordering.
 19. A network computer for visualizing data, comprising: a transceiver that communicates over the network; a memory that stores at least instructions; and one or more processors that execute instructions that perform actions, including: generating a graphical user interface (GUI) that includes a graph panel and a visualization panel, wherein the graph panel and the visualization panel are arranged to receive one or more inputs or interactions; providing a data model that includes a plurality of records, wherein one or more portions of the data model are displayed in the visualization panel; providing input information based on one or more inputs to the visualization panel, wherein the input information specifies one or more portions of the data model; determining one or more transform models based on the one or more specified portions of the data model, wherein the determined one or more transform models include a model interface that accepts the input information based on one or more acceptable characteristics of inputs from the one or more specified portions of the data model; employing the model interface of the one or more transform models to determine eligibility of each transform model to transform one or more data objects for the one or more portions of the data model; in response the model interface determining the one or more transform models are ineligible to provide transformations for the one or more data objects, employing the user interface to provide one or more of playing an audio cue or displaying a visual cue and provide one or more recommendations to remedy the ineligibility that are determined by the model interface; employing one or more eligible transform models to perform further actions, including: generating one or more graph objects based on the data model and the input information, wherein the one or more graph objects are included in a graph model that is comprised of nodes that represent the one or more graph objects and one or more edges that represent one or more relationships between two or more graph objects, and wherein at least one of the one or more relationships is unrepresented in the data model; and evaluating one or more attributes of a first portion of the one or more graph objects with one or more other attributes of a second portion of the one or more graph objects; and determining the one or more relationships based on the evaluation satisfying one or more conditions specified in the one or more eligible transform models; and executing a query based on the graph model to provide one or more results from the data model, wherein the one or more results are displayed in a visualization to one or more users.
 20. The network computer of claim 19, wherein generating the one or more graph objects, further comprises: determining one or more operations that are specified by the one or more eligible transform models; and executing the one or more operations on the data model to generate the one or more graph objects.
 21. The network computer of claim 19, wherein providing the data model, further comprises, providing one or more data sources based on one or more of a database, a columnar data store, or structured text files.
 22. The network computer of claim 19, wherein the one or more processors execute instructions that perform actions, further comprising: providing other input information based on one or more inputs to the graph panel, wherein the other input information specifies one or more of the one or more nodes or the one or more edges of the graph model; determining one or more graph transform models based on the other input information; and modifying the graph model based on the one or more graph transform models.
 23. The network computer of claim 19, wherein executing the query based on the graph model, further comprises: determining one or more graph objects that correspond to the query; determining one or more records from the data model based on the one or more graph objects that correspond to the query; and generating the one or more results to the query based on the one or more records.
 24. The network computer of claim 19, wherein the one or more processors execute instructions that perform actions, further comprising: determining two or more eligible transform models based on the input information; rank ordering the two or more eligible transform models based on a weight score; and employing one or more transform models to generate the one or more graph objects based on the ranked ordering. 